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Explicitly Constrained Black-Box Optimization With Disconnected Feasible Domains Using Deep Generative Models

IEEE ACCESS 2022年 10卷 117501-117514页

作者： Sakamoto, Naoki Sato, Rei Fukuchi, Kazuto Sakuma, Jun Akimoto, Youhei Univ Tsukuba Dept Comp Sci Tsukuba Ibaraki 3058573 Japan Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki 3058573 Japan RIKEN Ctr Adv Intelligence Project Chuo ku Tokyo 1030027 Japan

We tackle explicitly constrained black-box continuous optimization problems in which the feasible domain forms a union of disconnected feasible subdomains. The decoder-based constraint-handling technique is a promising approach when the feasible domain is disconnected. However, the design of a reasonable decoder requires deep prior knowledge of the optimization problem to be solved and, hence, human effort. In this study, we investigated the usefulness of a deep neural network as a decoder and developed a training scheme for a deep neural network without prior information, such as a training dataset consisting of feasible and infeasible solutions required by existing decoder approaches. To stabilize the training of the deep generative model as the decoder, we propose decomposing the decoder into sub-models, introducing skip connections to each sub-model, and training the sub-models sequentially with separate loss functions. Numerical experiments using a test problem and a topology optimization problem show that the proposed method can find feasible domains with better objective function values and higher probability than both conventional decoder-based constraint-handling methods and non-decoder-based constraint-handling methods.

关键词： Black-box optimization constraint handling deep learning disconnected feasible domain evolutionary computation explicit constraint generative models

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Ensemble Many-Objective Optimization Algorithm Based on Voting Mechanism

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 1716-1730页

作者： Qiu, Wenbo Zhu, Jianghan Wu, Guohua Chen, Huangke Pedrycz, Witold Suganthan, Ponnuthurai Nagaratnam Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Nanyang Technol Univ Sch Elect Elect Engn Singapore 639798 Singapore

Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-objective optimization problems (MaOPs). Generally, different solution-sorting methods possess different advantages in dealing with distinct MaOPs. Focusing on this characteristic, this article proposes a general voting-mechanism-based ensemble framework (VMEF), where different solution-sorting methods can be integrated and work cooperatively to select promising solutions in a more robust manner. In addition, a strategy is designed to calculate the contribution of each solution-sorting method and then the total votes are adaptively allocated to different solution-sorting methods according to their contribution. Solution-sorting methods that make more contribution to the optimization process are rewarded with more votes and the solution-sorting methods with poor contribution will be punished in a period of time, which offers a good feedback to the optimization process. Finally, to test the performance of VMEF, extensive experiments are conducted in which VMEF is compared with five state-of-the-art peer many-objective EAs, including NSGA-III, SPEA/R, hpaEA, BiGE, and grid-based evolutionary algorithm. Experimental results demonstrate that the overall performance of VMEF is significantly better than that of these comparative algorithms.

关键词： Optimization Sorting Sociology Convergence Heuristic algorithms evolutionary computation Transportation Ensemble framework evolutionary optimization many-objective optimization solution-sorting methods voting

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A virtual sensor for backlash in robotic manipulators

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JOURNAL OF INTELLIGENT MANUFACTURING 2022年第7期33卷 1921-1937页

作者： Giovannitti, Eliana Nabavi, Sayyidshahab Squillero, Giovanni Tonda, Alberto COMAU Ind Automat & Robot Grugliasco Italy Politecn Torino Turin Italy INRAE UMR MIA 518 Paris France

Gear backlash is a quite serious problem in industrial robots, it causes vibrations and impairs the robot positioning accuracy. Backlash estimation allows targeted maintenance interventions, preserving robot performances and avoiding unforeseen equipment breakdowns. However, a direct measure of the backlash is hard to obtain, and dedicated auxiliary sensors are required for the measurement. This paper presents a method for estimating backlash in robotic joints that does not require the installation of extra devices. It only relies on data gathered from the motor encoder, which is always present in a robotic joint. The approach is based on the observation of a characteristic vibration pattern arising on the motor speed signal when backlash affects the joint transmission. By looking at the amplitude of this vibration some information about the entity of the backlash in the joint is gathered. Experimental results on simulated data are reported in the study to show the robustness of the method, also with respect of noise. Furthermore, tests on real-world data, gathered from robots installed in a production plant, demonstrate the efficacy of the technique. The approach is cost-effective, fast, and easily automatable, therefore convenient for the industrial world.

关键词： computational Intelligence Backlash Virtual Sensors evolutionary computation

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An adaptive replacement strategy-incorporated particle swarm optimizer for wind farmlayout optimization

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ENERGY CONVERSION AND MANAGEMENT 2022年 269卷

作者： Lei, Zhenyu Gao, Shangce Wang, Yirui Yu, Yang Guo, Lijun Univ Toyama Fac Engn Toyama 9308555 Japan Ningbo Univ Fac Elect Engn & Comp Sci Ningbo University 315211 Zhejiang Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

The wind farm layout optimization (WFLO) aims to maximize power generation of a wind farm by optimizing the location of wind turbines. Traditional mathematical methods cannot provide a satisfactory solution for a wind farm due to the high complexity of the problem. Meta-heuristic algorithms have been used to optimize it. Particularly, genetic algorithms (GA) have been widely used and obtained success in WFLO problems. However, GA still suffers from the issues of insufficient optimization efficiency. In this study, a genetic learning particle swarm optimization with an adaptive strategy, termed AGPSO, is proposed to optimize WFLO problems. The strategy adaptively adjusts the location of the worst turbine to improve the conversion efficiency of a wind farm. Four wind scenarios, including single wind speed with single wind direction, single wind speed with uniform multiple wind directions, single wind speed with nonuniform multiple directions, and multiple wind speeds with multiple wind directions scenarios ones, are utilized to verify the effectiveness of AGPSO and the effect of different wind scenarios for it. Twelve constraints and three different scales are used to further verify the robustness of AGPSO and the effect of wind turbine location on WFLO problems. Extensive numerical experiment results demonstrate that AGPSO performs significantly better than other twelve state-of-the-art competitors in terms of conversion efficiency under different wind farms, wind scenarios, and constraints. AGPSO obtains the best average of 89.92%, 92.90%, 95.39%, and 90.75% conversion efficiency in a wind farm with 25 wind turbines under four wind scenarios, respectively.

关键词： Wind farm layout optimization Particle swarm optimization Wake effect evolutionary computation Adaptive replacement strategy

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WiSM: Windowing Surrogate Model for Evaluation of Curvature-Constrained Tours With Dubins Vehicle

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第2期52卷 1302-1311页

作者： Drchal, Jan Faigl, Jan Vana, Petr Czech Tech Univ Fac Elect Engn Prague 16627 Czech Republic

Dubins tours represent a solution of the Dubins traveling salesman problem (DTSP) that is a variant of the optimization routing problem to determine a curvature-constrained shortest path to visit a set of locations such that the path is feasible for Dubins vehicle, which moves only forward and has a limited turning radius. The DTSP combines the NP-hard combinatorial optimization to determine the optimal sequence of visits to the locations, as in the regular TSP, with the continuous optimization of the heading angles at the locations, where the optimal heading values depend on the sequence of visits and vice versa. We address the computationally challenging DTSP by fast evaluation of the sequence of visits by the proposed windowing surrogate model (WiSM), which estimates the length of the optimal Dubins path connecting a sequence of locations in a Dubins tour. The estimation is sped up by a regression model trained using close to optimum solutions of small Dubins tours that are generalized for large-scale instances of the addressed DTSP utilizing the sliding-window technique and a cache for already computed results. The reported results support that the proposed WiSM enables fast convergence of a relatively simple evolutionary algorithm to high-quality solutions of the DTSP. We show that with an increasing number of locations, our algorithm scales significantly better than other state-of-the-art DTSP solvers.

关键词： Optimization Iris computational modeling Turning Convergence evolutionary computation Sociology Dubins touring problem (DTP) Dubins traveling salesman problem (DTSP) Dubins vehicle surrogate model

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A Novel Multi-Objective Optimization Based evolutionary Algorithm for Optimize the Services of Internet of Everything

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IEEE ACCESS 2022年 10卷 106798-106811页

作者： Singh, Shailendra Pratap Dhiman, Gaurav Viriyasitavat, Wattana Kautish, Sandeep Galgotias Univ Sch Comp Sci & Engn Greater Noida 203201 India Lebanese Amer Univ Dept Elect & Comp Engn Byblos 1701 Lebanon Chandigarh Univ Ctr Res & Dev Dept Comp Sci & Engn Gharuan 140413 Mohali India Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun 248002 Uttar Pradesh India Univ Int Iberoamer Dept Project Management Campeche 24560 Mexico Chulalongkorn Univ Fac Commerce & Accountancy Dept Stat Business Informat Technol Div Bangkok 10330 Thailand Lord Buddha Educ Fdn LBEF Campus Kathmandu 44600 Nepal

In the new era, the Internet of Everything (IoE) provides distributed services like data, processes, people, and things, etc. The services to the connected IoE significantly increase the time of service, workload, energy consumption, and delay. These objectives conflict with each other. To address the issue, a novel multi-objective based evolutionary algorithm is proposed. In the proposed method, a new rapid mutation operator is incorporated with multi-objective differential evolution (MODE) to overcome the stagnation of the local optimum. The proposed method to maintain the diversity and enhance the convergence speed of the existing MODE algorithm is described. The proposed method provides more diversity and convergence speed for choosing better candidate solutions. The addition of the proposed method is evaluated with the application of IoE services. We have designed the two objective and three objective-based IoE services scenarios. Furthermore, the proposed method optimizes services like service cost, service delay, and the lifetime of sensors. It is interesting to observe that the proposed approach better performs the most recent state-of-the-art multiobjective evolutionary algorithms.

关键词： Internet of Things Optimization evolutionary computation Delays Costs Heuristic algorithms Cloud computing Adaptation differential evolution multi-objective evolutionary algorithms Internet of Everything

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Impact of Boundary Control Methods on Bound-Constrained Optimization Benchmarking

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IEEE TRANSACTIONS ON evolutionary computation 2022年第6期26卷 1271-1280页

作者： Kadavy, Tomas Viktorin, Adam Kazikova, Anezka Pluhacek, Michal Senkerik, Roman Tomas Bata Univ Zlin Fac Appl Informat Zlin 76001 Czech Republic

Benchmarking various metaheuristics and their new enhancements, strategies, and adaptation mechanisms has become standard in computational intelligence research. Recently, many challenges and issues regarding fair comparisons and recommendations toward good practices for benchmarking of metaheuristic algorithms, have been identified. This article is aimed at an important issues in metaheuristics design and benchmarking, which are boundary strategies or boundary control methods (BCMs). This work aims to investigate whether the choice of a BCM could significantly influence the performance of competitive algorithms. The experiments encompass the top three performing algorithms from IEEE CEC competitions 2017 and 2020 with six different BCMs. We provide extensive statistical analysis and rankings resulting in conclusions and recommendations for metaheuristics researchers and possibly also for the future direction of benchmark definitions. We conclude that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics, since the BCM setting could impact the optimization method performance.

关键词： Benchmark testing boundary control method (BCM) computational intelligence evolutionary computation optimization performance evaluation

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A Comparative Study of Dispatching Rule Representations in evolutionary Algorithms for the Dynamic Unrelated Machines Environment

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IEEE ACCESS 2022年 10卷 22886-22901页

作者： Planinic, Lucija Backovic, Hrvoje Durasevic, Marko Jakobovic, Domagoj Univ Zagreb Fac Elect Engn & Comp Zagreb 10000 Croatia Visage Technol Zagreb 10000 Croatia

Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary computation methods. In previous research, genetic programming has been the most commonly used method for automatically designing new dispatching rules. However, there are many other evolutionary methods that use representations other than genetic programming that can be used to create dispatching rules. Some, such as gene expression programming, have already been used successfully, while others, such as Cartesian genetic programming or grammatical evolution, have not yet been used to generate dispatching rules. In this paper, six different methods (genetic programming, gene expression programming, Cartesian genetic programming, grammatical evolution, stack representation, and analytic programming) for generating dispatching rules for the unrelated machines environment are tested and the results for various scheduling criteria are analysed. It is also analysed how different individual sizes in the tested methods affect the performance and average size of the generated dispatching rules. The results show that, with the exception of grammatical evolution and analytic programming, all tested methods perform quite similarly, with results depending on the selected scheduling criterion. The results also show that Cartesian genetic programming is the most resistant to the occurrence of bloat and evolves dispatching rules with the smallest average size.

关键词： Dispatching Dynamic scheduling Genetic programming Programming Processor scheduling Schedules evolutionary computation Unrelated machines environment scheduling solution representations dispatching rules

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A Review on Swarm Intelligence and evolutionary Algorithms for Solving the Traffic Signal Control Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第1期23卷 48-63页

作者： Shaikh, Palwasha W. El-Abd, Mohammed Khanafer, Mounib Gao, Kaizhou Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Amer Univ Kuwait Dept Engn Safat 13034 Kuwait Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Taipa 999078 Macao Peoples R China

The rapid development of urban cities coupled with the rise in population has led to an exponentially growing number of vehicles on the roads for the latter to commute. This is adding to the already overbearing problem of traffic congestion. Short term, costly and short-sighted solutions of road infrastructure expansions are no longer suitable. One effective method of road resource allocation is focusing on the widely used traffic signal controllers' timing schedules. Searching for a suitable or an optimal schedule for the prior via brute force to ease traffic congestion might not be the most elegant or feasible solution. Nature-inspired algorithms including evolutionary and swarm intelligence algorithms are gaining a lot of momentum. Many of these algorithms have been used in the last two decades to address different applications in the smart city era including traffic signal control (TSC). This paper conducts a comprehensive literature review on applications of evolutionary and swarm intelligence algorithms to TSC. Surveyed work is categorized based on the set of decision variables, optimization objective(s), problem modeling and solution encoding. The paper, based on gaps identified by the conducted review, identifies promising future research directions and discusses where the future research is headed.

关键词： Optimization Roads Particle swarm optimization Vehicles Green products Urban areas evolutionary computation evolutionary algorithm swarm Intelligence meta-heuristics optimization traffic signal control traffic intersection single-objective multi-objective bi-level optimization

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evolutionary Analysis of Cloud Manufacturing Platform Service Innovation Based on a Multiagent Game Perspective

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IEEE ACCESS 2022年 10卷 104543-104554页

作者： Xin, Yuhong Liu, Dehui Zhou, Xindi Guangdong Polytech Normal Univ Sch Comp Sci Guangzhou 5106652 Peoples R China Jinan Univ Sch Econ Guangzhou 510630 Peoples R China

With the rapid development of cloud computing, the Internet of Things (IoT) and other new generation information technologies, a new service-oriented networked intelligent manufacturing mode-cloud manufacturing (CMfg), is emerged. In the CMfg paradigm, cloud manufacturing platform service innovation is an effective way to improve the service function, service quality and service efficiency of cloud manufacturing platforms. However, the service innovation of cloud manufacturing platforms is complex system engineering, which needs to consider many participants with independent interests simultaneously. If one party's behaviour strategy changes, the whole system will be affected. In order to adapt the decision-making management of manufacturing service innovation in cloud manufacturing system, to provide the effective decision-making support for service innovation of cloud manufacturing system in uncertain environment, to explore the interaction mechanism and equilibrium strategy of various stakeholders in the evolution process of cloud manufacturing system. This research constructs a tripartite evolutionary game model among participants based on evolutionary game theory, Analyses the influence of participants' strategy choices, The Cloud manufacturing platform's service quality coefficients and users' preference Coefficients on the equilibrium state stability of cloud manufacturing systems, and uses MATLAB software for numerical simulation. The results show that the evolutionary stability strategy of cloud manufacturing enterprises plays a decisive role in the service innovation of the cloud manufacturing platform. When cloud manufacturing enterprises choose a noncooperation strategy, the evolutionary stability strategy of the cloud manufacturing platform is innovation. When cloud manufacturing enterprises choose a cooperation strategy, the evolutionary stability strategy of the cloud manufacturing platform is affected by the platform's service quality coefficient and

关键词： Manufacturing Cloud computing Multi-agent systems Games Ecosystems Dynamic scheduling Analytical models evolutionary computation Cloud manufacturing platform service innovation tripartite evolutionary game multiagent perspective

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