Optimization models treat natural selection as a process tending to produce maximal adaptedness to the environment, measured on some "criterion scale" defining the optimal phenotype. These models are descriptively adequate if they describe the outcomes of evolutionary processes. They are dynamically adequate if the variables which describe the outcomes also are responsible for those evolutionary outcomes. Optimality models can be descriptively adequate, but dynamically unrealistic. Relying on cases from evolutionary ecology, I provide reasons to question the dynamic adequacy of optimality (...) models, and offer reasons for distinguishing, at least at a theoretical level, between satisficing and optimizing. (shrink)

The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon's work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (2) the revealed preference (...) theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though "solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks... it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs." Presenting powerful, proven optimization techniques for (...) logic inference problems, Chandru and Hooker show how optimization models can be used not only to solve problems in artificial intelligence and mathematical programming, but also have tremendous application in complex systems in general. They survey most of the recent research from the past decade in logic/optimization interfaces, incorporate some of their own results, and emphasize the types of logic most receptive to optimization methods-propositional logic, first order predicate logic, probabilistic and related logics, logics that combine evidence such as Dempster-Shafer theory, rule systems with confidence factors, and constraint logic programming systems. Requiring no background in logic and clearly explaining all topics from the ground up, Optimization Methods for Logical Inference is an invaluable guide for scientists and students in diverse fields, including operations research, computer science, artificial intelligence, decision support systems, and engineering. (shrink)

We reopen the investigation into the formal and conceptual relationship between bidirectional optimality theory (Blutner in J Semant 15(2):115–162, 1998 , J Semant 17(3):189–216, 2000 ) and game theory. Unlike a likeminded previous endeavor by Dekker and van Rooij (J Semant 17:217–242, 2000 ), we consider signaling games not strategic games, and seek to ground bidirectional optimization once in a model of rational step-by-step reasoning and once in a model of reinforcement learning. We give sufficient conditions for equivalence of bidirectional (...) optimality and the former, and show based on numerical simulations that bidirectional optimization may be thought of as a process of reinforcement learning with lateral inhibition. (shrink)

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the (...) relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. (shrink)

A pioneering look at the fundamental role of logic in optimizationand constraint satisfaction While recent efforts to combine optimization and constraintsatisfaction have received considerable attention, little has beensaid about using logic in optimization as the key to unifying thetwo fields. Logic-Based Methods for Optimization develops for thefirst time a comprehensive conceptual framework for integratingoptimization and constraint satisfaction, then goes a step furtherand shows how extending logical inference to optimization allowsfor more powerful as well as flexible modeling and solutiontechniques. Designed to (...) be easily accessible to industryprofessionals and academics in both operations research andartificial intelligence, the book provides a wealth of examples aswell as elegant techniques and modeling frameworks ready forimplementation. Timely, original, and thought-provoking,Logic-Based Methods for Optimization: * Demonstrates the advantages of combining the techniques inproblem solving * Offers tutorials in constraint satisfaction/constraintprogramming and logical inference * Clearly explains such concepts as relaxation, cutting planes,nonserial dynamic programming, and Bender's decomposition * Reviews the necessary technologies for software developersseeking to combine the two techniques * Features extensive references to important computationalstudies * And much more. (shrink)

The Genetic Algorithm (GA) and Simulated Annealing (SA), two techniques for global optimization, were applied to a reduced (simplified) form of the phase problem (RPP) in computational crystallography. Results were compared with those of "enhanced pair flipping" (EPF), a more elaborate problem-specific algorithm incorporating local and global searches. Not surprisingly, EPF did better than the GA or SA approaches, but the existence of GA and SA techniques more advanced than those used in this study suggest that these techniques still hold (...) promise for phase problem applications. The RPP is, furthermore, an excellent test problem for such global optimization methods. (shrink)

Optimization for vertical vibration performance of a rail-train coupling system is investigated in this paper with the introduction of inerters for both primary and secondary suspensions. A model of a typical Chinese passenger train that travels on a traditional rail with track, sleepers, and ballast is simulated. The goal is to improve the ride quality for the train and vibration attenuation for the rail system in response to track irregularities. Optimizations for only inertance and all suspension parameters are carried out (...) by the particle swarm algorithm. Performance benefits for both the train and the rail system are demonstrated and suspension layouts with inerters connected in parallel and series are compared with the traditional one in both time domain and frequency domain. (shrink)

This paper explores an approach for formulating a prescriptive theory of the firm that integrates economic and ethical criteria to guide strategic and operationalconduct of managers. A prescriptive theory posits goal optimization. A “constrained multiple goal optimization” approach models the firm as a broad set of multiple goals and multiple constraints, the latter both internal and external. An exploration begins with no assumptions concerning whether economics and ethics are compatible or antithetical. If the two approaches are mutually reinforcing, a win-win (...) situation obtains. If the two approaches are in win-lose conflict, a key issue is which approach is hierarchically superior or whether the two approaches can be weighted relatively. (shrink)

River water pollution by wastewater can cause significant negative impact on the aquatic sustainability. Hence, accurate modeling of this complicated system and its cost-effective treatment and reuse decision is very important because this optimization process is related to economic expenditure, societal health, and environmental deterioration. In order to optimize this complex system, we may consider three treatment or reuse options such as microscreening filtration, nitrification, and fertilization-oriented irrigation on top of two existing options such as settling and biological oxidation. The (...) objective of this environmental optimization is to minimize the economic expenditure of life cycle costs while satisfying the public health standard in terms of groundwater quality and the environmental standard in terms of river water quality. Particularly, this study improves existing optimization model by pinpointing the critical deficit location of dissolved oxygen sag curve by using analytic differentiation. Also, the proposed formulation considers more practical constraints such as maximal size of irrigation area and minimal amount of filtration treatment process. The results obtained by using an evolutionary algorithm, named a parameter-setting-free harmony search algorithm, show that the proposed model successfully finds optimal solutions while conveniently locating the critical deficit point. (shrink)

This article argues that evolutionary models based on selection validate, under appropriate conditions, the relevance of optimality as an explanatory mechanism in rational choice theory. The reason is that these frameworks share the mechanism that drives the results, namely, optimization, even if they situate it at different levels. The consequences of our argument are twofold. First, it resolves the tension between those predictions of rational choice theory that are accurate and the evidence showing that individuals seldom optimize. Second, it relativizes (...) the explanatory import of rationality without diminishing the role of optimization as a mechanism. (shrink)

This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

Freight transportation plays a critical role in improving company performance in the modern manufacturing industry. To reduce costs, companies must take advantage of the use of large vehicles. It caused fewer deliveries, but inventory costs and degradation quality are high. One of the joint economic lot size problems in supply chain is Integrated Single-Vendor Single-Buyer Inventory Problem. This study developed the I-SVSB-IP model that considers raw materials’ exponential quality degradation and transportation costs. The objective function of this research was to (...) maximize the Joint Total Profit. Three decision variables used were inventory cycle time, raw material ordering frequency, and frequency of delivery of finished products to buyers. This study proposed a sophisticated Chimp Optimization Algorithm procedure to solve the I-SVSB-IP problem. A case study on the food industry in Indonesia was presented to optimize the I-SVSB-IP. The results showed that the ChOA procedure had produced an optimal solution compared to the state-of-the-art algorithm. This study also demonstrated a sensitivity analysis of decision and transportation variables to cost, revenue, and JTP. The results show that increasing transport frequency of ordering raw materials and finished products to buyers enhances the total cost and reduces joint total profit. In addition, increasing the rate of quality degradation of raw materials reduces JTP. (shrink)

Optimization is about finding the best available object with respect to an objective function. Mathematics and quantitative sciences have been highly successful in formulating problems as optimization problems, and constructing clever processes that find optimal objects from sets of objects. As computers have become readily available to most people, optimization and optimized processes play a very broad role in societies. It is not obvious, however, that the optimization processes that work for mathematics and abstract objects should be readily applied to (...) complex and open social systems. In this paper we set forth a framework to understand when optimization is limited, particularly for complex and open social systems. (shrink)

We design and implement an intelligent IoT-based motion monitoring system to realize the monitoring of three important parameters, namely, the type of movement, the number of movements, and the period of movement in physical activities, and optimize the system to support the simultaneous use by multiple users. Considering the motion monitoring scenario for smart fit, the framework of an IoT-based motion monitoring system is proposed. The framework contains components such as active acquisition nodes, wireless access points, data processing servers, and (...) terminals. In terms of algorithm optimization, research related to active pattern recognition and periodic calculation methods is conducted. For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. For period calculation, a method based on over-zero detection and wavelet transform is proposed to count the number of actions and calculate the period of each action. In 100 times action cycle calculation experiments, the count statistic calculation method achieves 100% calculation accuracy and the active cycle calculation results are close to the real value, which proves the effectiveness of the cycle calculation method. The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training. (shrink)

This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.

With the increasing number of electric vehicles, the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations (...) and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy. (shrink)

This paper chooses car travel and bus travel as the research objects, establishes a dual-mode equilibrium model based on the bottleneck model, and compares the travel characteristics of the no-toll and fine-toll schemes. We find that the fine-toll scheme can eliminate the queuing time at the bottleneck, but it also increases the congestion risk cost of bus travel. In order to eliminate the queuing time at the bottleneck and reduce the congestion risk cost of bus travel without increasing the car (...) travel cost and bus travel cost, we propose an optimization scheme of fine toll and bus departure quantity and analyze its travel characteristics theoretically. Through the numerical example, we calculate and analyze the equilibrium results of no-toll scheme, fine-toll scheme, and optimization scheme of fine toll and bus departure quantity. The results indicate that the optimization scheme of fine toll and bus departure quantity can help travelers to choose a reasonable travel mode and travel time to travel in the rush hour. (shrink)

With the contradiction between supply and demand being intensified, the unreasonable crude oil trade structure has become increasingly prominent. Therefore, optimizing the supply and demand structure of the global crude oil trade is an urgent problem, which has become one of the crucial factors affecting the energy strategy and economic development of each country. This paper builds an optimization model that aims at minimizing crude oil trade costs based on the complex network theory. Meanwhile, an optimal solution generation approach is (...) proposed and developed in this paper. Compared with the preoptimized trade network, the proposed model can effectively reduce the trade cost. By topological analysis of the trade network and its optimal configuration, we obtain that both the preoptimized and optimized crude oil trade networks follow power-law distribution. By using the minimum spanning tree, we find that the major crude oil net exporting countries have the most significant influence and are at the core in the optimized trade structure. This work focuses on the sustainable development of the global crude oil trade and provides a fresh perspective for the optimal crude oil trade system. Moreover, the methodology and model may be applied in the investigation of optimization for other energy system structures. (shrink)

The purpose of the study is to reveal theoretical and methodological approaches to optimizing the formation of cash flows of enterprises. The article presents an analysis of the process of integrating the optimization of the company's cash flows into the overall sequence of their management, as well as the conditions for the application of complex measures for subsequent financial recovery. The scientific novelty of the obtained results lies in the development and refinement of theoretical provisions, schemes for optimizing cash flows (...) and determining the directions for evaluating the optimization of cash flows. As a result, the main directions of optimization of cash flows are identified. As a result, it is revealed that the optimization process is formed on the basis of a logical sequence, the process of implementing management decisions regarding the optimization of the cash flows of the enterprise is justified. (shrink)

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence (...) as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions. (shrink)

Dry port construction can reduce the cost of container transportation, and its location is the focus of existing research. Considering dry port capacity limitations and scale advantages, this study calculates the costs associated with dry port construction and operations, transportation, time, and the environment and constructs a joint optimization model of the dry port location and transportation scheme to minimize the total cost. Taking 35 prefecture-level cities in Northeast China as the source of container goods and Dalian port as the (...) destination, this study conducts an empirical analysis using the Gurobi 9.0.2 optimizer of the AMPL software to solve the problem and takes the minimum total cost as the goal to select the best dry port and container transshipment scheme. The research draws the following conclusions. Seven dry ports also need to be built in the road-rail mode, which shares 82.76% of the container transshipment volume, to reduce the total transportation cost by approximately 21.67%. Although multimodal transport through dry ports increases the time cost slightly, it can significantly reduce the economic and environmental costs of container transportation. (shrink)

The teaching management department carries all the work related to teaching in the whole school. A scientific, efficient, and complete teaching management system cannot only help the teaching management department improve work efficiency and quality but also greatly reduce many problems caused by manual labour risk. This paper designs and implements a teaching management system based on an improved association rule algorithm. First, aiming at the low efficiency of the Apriori algorithm for mining association rules, an association rule model based (...) on interest is proposed. Second, use the MapReduce calculation model to partition the transaction database, then use the improved Apriori optimization algorithm for mining, and finally merge the mining results to obtain frequent itemsets. Through experiments, the optimized algorithm has greatly improved selection mining and computing time than traditional algorithms. (shrink)

The optimal design of cables under selfweight and concentrated loads is considered and the maximum feasible span for a given permissible stress is evaluated. All solutions are obtained in a closed analytical form.

With the rapid development of China’s social economy and the improvement of the level of urbanization, urban transportation has also been greatly developed. With the booming development of the internet and the sharing economy industry, shared bicycles have emerged as the times requirement. Shared bicycles are a new type of urban transportation without piles. As a green way of travel, shared bicycles have the advantages of convenience, fashion, green, and environmental protection. However, many problems have also arisen in the use (...) of shared bicycles, such as man-made damage to the vehicle, the expiration of the service life of the vehicle, etc. These problems are unavoidable, and the occurrence of these failure problems will also cause serious harm to the use of shared bicycles. This article aims to study the path optimization of shared bicycles considering the recovery of faulty vehicles during dispatching. Based on the K-means spatial data clustering algorithm, a path optimization experiment of shared bicycle recycling scheduling considering the recycling of faulty vehicles is carried out. The experiment concluded that the shared bicycle recycling scheduling path based on K-means clustering planning significantly reduces the total time spent and the total cost of performing recycling scheduling tasks. Among them, the unit price of recycling and dispatching of each faulty shared bicycle has dropped by 4.1 yuan compared with the market unit price. The conclusion shows that the shared bicycle recycling scheduling path considering faulty vehicle recycling based on K-means clustering algorithm has been greatly optimized. (shrink)

Wheels are the key components of a train, and the shape of the wheel flange should be maintained to ensure the security of train operations. As a method to maintain the shape at the cost of the diameter size, reprofiling has significant impacts on the lifecycle of a train. A wheel model is built in this paper based on the analysis of the wheel wear features and datasets from Taiyuan locomotives. With the decision variables T i, T i ′, which (...) describe the reprofiling strategy, we formulate a multiobjective optimization problem simultaneously minimizing the reprofiling numbers and maximizing the serving years. To find the solutions of the multiobjective model, the NSGA-II is extended with an alteration of the crowding distance calculation and genetic operators. The improved NSGA-II performs better than other approaches. Meanwhile, outstanding solutions with longer servicing years and less reprofiling are listed in this paper. Our study reveals the relationship between the diameter, flange thickness, and their individual attrition rates and proposes a wear model, multiobjective model, and improved NSGA-II. Compared with existing reprofiling strategies, the strategy recommended in our work can significantly increase the lifecycle of the wheel coupled with a low repair frequency. (shrink)

This work is developed to improve the current quality of e-commerce logistics services. From the perspective of multiple consumer psychology, based on e-commerce, consumer psychology, and other related theories, vegetable e-commerce B is selected as the research object. The commodity quality, accuracy, and timeliness of commodity distribution and other factors of e-commerce B are discussed through questionnaire survey. Then, according to customers’ opinions about “e-commerce B’s distribution and professional aspects that need to be improved,” the research is conducted. Finally, the (...) direction of follow-up optimization is proposed from four different perspectives of multiple consumption psychology. The research results show that more than 89% of the surveyed customers believe that e-commerce B does a good job in terms of commodity quality, accuracy, and timeliness of commodity distribution, and has a high level of logistics service. However, 26.34% of customers hope that e-commerce B can strengthen the protection of personal privacy, 24.97% hope that the platform can add “delay insurance” for goods, and 39.54% hope that the logistics information of purchased goods can be updated in real time. Therefore, e-commerce B needs to be optimized and improved continuously in the future development. Therefore, research on the optimization of logistics service quality of e-commerce is performed under multiple consumption psychology, which provides certain help for the rapid development of subsequent e-commerce. (shrink)

Mobile health applications (‘health apps’) that promise the user to help her with some aspect of her health are very popular: for-profit apps such as MyFitnessPal, Fitbit, or Headspace have tens of millions of users each. For-profit health apps are designed and run as optimization systems. One would expect that these health apps aim to optimize the health of the user, but in reality they aim to optimize user engagement and, in effect, conversion. This is problematic, I argue, because digital (...) health environments that aim to optimize user engagement risk being manipulative. To develop this argument, I first provide a brief analysis of the underlying business models and the resulting designs of the digital environments provided by popular for-profit health apps. In a second step, I present a concept of manipulation that can help analyze digital environments such as health apps. In the last part of the article, I use my concept of manipulation to analyze the manipulative potential of for-profit health apps. Although for-profit health can certainly empower their users, the conditions for empowerment also largely overlap with the conditions for manipulation. As a result, we should be cautious when embracing the empowerment discourse surrounding health apps. An additional aim of this article is to contribute to the rapidly growing literature on digital choice architectures and the ethics of influencing behavior through such choice architectures. I take health apps to be a paradigmatic example of digital choice architectures that give rise to ethical questions, so my analysis of the manipulative potential of health apps can also inform the larger literature on digital choice architectures. (shrink)

David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for cognitive (...) mechanisms just as anti-adaptationists have argued they are inappropriate for other physiological mechanisms. I will discuss the nature of optimizing assumptions and theories. Considering various difficulties in identifying and assessing optimizing assumptions, I will suggest that Marr's theory is not purely an optimizing theory and that reaction to Marr on this issue prompts interesting considerations for the development of inter-disciplinary constraints in the cognitive and brain sciences. (shrink)

As a universal language in the world, English has become a necessary language communication tool under the globalization of trade. Intelligent, efficient, and reasonable English language-assisted learning system helps to further improve the English ability of language learners. English online learning dictionary, as an important query tool for English learners, is an important part of English online learning. This paper will optimize the design of English online learning dictionary system based on multiagent architecture. Based on the hybrid multiagent cooperative algorithm, (...) this paper will improve the disadvantages of the online English learning dictionary system and propose an appropriate dictionary application evaluation function. At the same time, an improved reinforcement learning algorithm is introduced into the corresponding English online learning dictionary navigation problem so as to improve the efficiency of the online English learning dictionary system. English online learning dictionary is more intelligent and efficient. In this paper, the new online learning dictionary system optimization algorithm is proposed and compared with the traditional system algorithm. The experimental results show that the algorithm proposed in this paper solves the collaborative confusion problem of English learning online dictionary to a certain extent and further solves the corresponding navigation problem so as to improve the efficiency. (shrink)

GIS is important equipment in the substation system in a complex microgrid network. Due to the long-term service in harsh operation environments, the electrical performance of GIS equipment will be seriously affected. Therefore, the regular maintenance of GIS equipment in the substation system is a routine task so as to ensure the normal operation of the microgrid networks. The traditional method relies on manual labor, where not only with the low operation efficiency but also regarding some maintenance, labors cannot reach (...) the inside of the GIS equipment, which seriously restricted the completion of GIS equipment maintenance operation. Based on the above analysis, a basic configuration of a GIS equipment maintenance robot with smart structure, convenient control, and stable motion characteristic has been proposed in this paper. Through the optimization design of the chassis adsorption system, the GIS inspection robot can achieve hanging and vertical adsorption inside the GIS equipment and some special motion status. In this way, the entire GIS equipment can be inspected without blind spots. Through establishing the mechanical model of the GIS inspection robot under the hanging and vertical motion state, the robustness and stability of the GIS inspection robot in the special status have been analyzed, and the corresponding GIS inspection robot robust stability motion control method has also been proposed. Finally, through the integrated design of mechanical system and hardware and software control system, the GIS inspection robot physical prototype system has been developed and the maintenance operation experiment has been carried out in Hunan Electric Power Company Maintenance Company through simulating the internal GIS equipment. The physical prototype can realize the basic movement and special movement in internal GIS equipment without any blind spots and complete the inspection task of GIS equipment operation; the movement of GIS robot is dexterous and stable in the whole operation process, which has strong robust stability. The research of GIS inspection robot in the complex microgrid networks has important theoretical significance and practical application value for the intelligent operation and maintenance management of substation system in complex microgrid networks. (shrink)

This paper introduces the basic principle and application process of simulated annealing algorithm and improves the simulated annealing algorithm so that it can converge faster to get the new parameters of cultural and creative product design and make it more in line with the reality of engineering optimization. In the cultural creative industry, it is necessary to use the creatorʼs creativity and technology to derive and develop the original cultural resources with the help of various materialization means, to refine the (...) abstract and profound contents of cultural resources to make them more visualized, and to produce products with both spiritual and economic values through the multiple development and utilization of their intellectual property contents. By studying the feasibility analysis of the cultural and creative design, the design positioning of the cultural and creative products is determined. In this article, we use the simulated annealing method to simulate and analyse the condition values in the design process of cultural creative products. As the Internet era progresses, people have more diversified requirements for cultural and creative products, and they need to have innovative ideas to follow the trend, so the design of cultural and creative products can spread culture through different design expressions and media. As one of the important pillar industries for future development, cultural and creative products are characterized by a wide range of styles and practicality, which meet the aesthetic needs and fashion trends of modern people. (shrink)

In the evaluation of teaching quality, aiming at the shortcomings of slow convergence of BP neural network and easy to fall into local optimum, an online teaching quality evaluation model based on analytic hierarchy process and particle swarm optimization BP neural network is proposed. Firstly, an online teaching quality evaluation system was established by using the analytic hierarchy process to determine the weight of each subsystem and each index in the online teaching quality evaluation system and then combined with actual (...) experience, the risk value of each index was constructed according to safety regulations. The regression model is established through BP neural network, and the weight and threshold of the model are optimized by the particle swarm algorithm. Based on the online teaching quality evaluation model of BP neural network, the parameters of the model are constantly adjusted, the appropriate function is selected, and the particle swarm algorithm which is used in the training and learning process of the neural network is optimized. The scientificity of the questionnaire was verified by reliability and validity test. According to the scoring results and combined with the weight coefficient of each indicator in the online course quality evaluation index system, the key factors affecting the quality of online courses were obtained. Based on the survey data, descriptive statistics, analysis of variance, and Pearson’s correlation coefficient method are used to verify the research hypothesis and obtain valuable empirical results. By comparing the model with the standard BP model, the results show that the accuracy of the PSO-BP model is higher than that of the standard BP model and PSO-BP effectively overcomes the shortcomings of the BP neural network. (shrink)

Plant protection problems are simulated by a system of ordinary differential equations with given initial conditions. The sensitivity and resistance of pathogen subpopulations to fungicide mixtures, fungicide weathering, plant growth, etc. are taken into consideration. The system of equations is solved numerically for each set of initial conditions and parameters of the disease and fungicide applications. Optimization algorithms were investigated and a computer program was developed for optimization of these solutions. 14 typical cases of the disease were simulated and optimized (...) in order to determine optimal fungicide treatments. The optimized strategy for fungicide application differs considerably from the commonly used method and seems to be an important new principle in plant protection. The approach developed in this study may be useful for a wide spectrum of purposes in the simulation of leaf diseases. It may also help the biologist to decrease or pinpoint experimental work and analyze its results and is perspective for plant disease control. (shrink)

Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance (...) the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure. (shrink)

This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi-time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data-Driven Construction Scheduling and Multiscale Resource Allocation Problem. Based on likelihood robust optimization, (...) a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double-layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization and interior point method named MOPSO-interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO-interior point algorithm can reduce resource cost and improve the efficiency of resource utilization. (shrink)

Agents face serious obstacles to making optimal decisions. For instance, their cognitive limits stand in the way. John Pollock’s book, Thinking about Acting , suggests many ways of revising decision principles to accommodate human limits and to direct limited, artificial agents. The book’s main proposal is to replace optimization, or expected-utility maximization, with locally global planning. This essay describes optimization and locally global planning, and then argues that optimization among salient options has the virtues of locally global planning without certain (...) drawbacks. Although it does not endorse locally global planning, it recommends that decision theory incorporate some of the book’s ideas about settling for improvements when optimization among all options is unrealistic. (shrink)

We introduce and study certain classes of optimization problems over the real numbers. The classes are defined by logical means, relying on metafinite model theory for so called R-structures . More precisely, based on a real analogue of Fagin's theorem [12] we deal with two classes MAX-NPR and MIN-NPR of maximization and minimization problems, respectively, and figure out their intrinsic logical structure. It is proven that MAX-NPR decomposes into four natural subclasses, whereas MIN-NPR decomposes into two. This gives a real (...) number analogue of a result by Kolaitis and Thakur [13] in the Turing model. Our proofs mainly use techniques from [17]. Finally, approximation issues are briefly discussed. (shrink)