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

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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Decision Making in evolutionary Multiobjective Clustering: A Machine Learning Challenge

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IEEE ACCESS 2022年 10卷 117281-117303页

作者： Garza-Fabre, Mario Sanchez-Martinez, Aaron L. Aldana-Bobadilla, Edwin Landa, Ricardo CINVESTAV Ctr Res & Adv Studies Campus Tamaulipas Ciudad Victoria 87130 Tamaulipas Mexico Natl Council Sci & Technol Mexico CONACyT Mexico City 03940 DF Mexico

evolutionary multiobjective algorithms have become a popular choice to tackle the clustering problem. On the one hand, the simultaneous optimization of complementary clustering criteria offers an increased robustness to changes in data characteristics. On the other hand, the evolutionary search is able to approximate the Pareto optimal front and deliver a set of trade-offs between these criteria in a single algorithm execution. Decision making is the concluding stage of the pipeline, having as its goal the selection of a single, final solution from the set of candidate trade-offs produced. This is a complex task for which a definitive answer does not seem to be available, as the underlying assumptions of existing techniques may not hold for all applications. In this paper, we investigate an alternative approach to address this challenge: posing it as a learning problem. The key idea is to build a model that, given a proper characterization of solutions and their context (defined by the full approximation solution set and the specific clustering task at hand), is able to estimate quality and facilitate the identification of the best choice. To evaluate the suitability of this approach, we conduct a series of experiments over diverse synthetic and real-world datasets, including comparisons against a range of representative decision-making strategies from the literature. Our proposal exhibits greater flexibility in dealing with problems of varying characteristics, consistently outperforming the reference methods considered. This study demonstrates that it is possible to learn from the decision-making process in example settings and generalize the acquired knowledge to new scenarios.

关键词： Clustering algorithms multiobjective clustering decision making evolutionary computation machine learning pareto optimization

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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 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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An edge-driven multi-agent optimization model for infectious disease detection

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APPLIED INTELLIGENCE 2022年第12期52卷 14362-14373页

作者： Djenouri, Youcef Srivastava, Gautam Yazidi, Anis Lin, Jerry Chun-Wei SINTEF Digital Oslo Norway Brandon Univ Brandon MB Canada China Med Univ Taichung Taiwan Oslomet Oslo Norway Western Norway Univ Appl Sci Bergen Norway

This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity embedding networks, long-short term memory, and convolution neural networks, for accurately learning heterogeneous medical data in identifying disease infection. The multi-agent system is also consolidated for increasing the autonomy behaviours of the proposed framework, where each agent can easily share the derived learning outputs with the other agents in the system. Furthermore, evolutionary computation algorithms, such as memetic algorithms, and bee swarm optimization controlled the exploration of the hyper-optimization parameter space of the proposed framework. Intensive experimentation has been established on medical data. Strong results obtained confirm the superiority of our framework against the solutions that are state of the art, in both detection rate, and runtime performance, where the detection rate reaches 98% for handling real use cases.

关键词： Edge-Driven Infection disease Multi-agents system evolutionary computation

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Learning-based monarch butterfly optimization algorithm for solving numerical optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第5期34卷 3939-3957页

作者： Ghetas, Mohamed NUB Nanda Univ Fac Comp Sci New Beni Suef City Egypt

Many optimization algorithms have been used to solve complex real-world problems, motivating many scientists to improve various optimization algorithms. The monarch butterfly optimization algorithm MBO has been proven to have good performance and is an effective tool for solving many problems. However, the migration and updating operators mainly learn from the global best and are often trapped in local optima as well as premature convergence in many optimization problems. This work introduces an adaptive learning strategy and elitism strategy namely ALMBO to enhance the exploration capacity of the algorithm and ensure that the fittest individuals are retained in the next generation. The experimental results on fourteen benchmark functions show that compared with the two representative MBO algorithm and other methods, ALMBO performs much better than the others on mean values, best values, standard deviation and convergence speed.

关键词： Monarch butterfly optimization evolutionary computation Butterfly adjusting operator Benchmark problems

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Fixed-Target Runtime Analysis

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ALGORITHMICA 2022年第6期84卷 1762-1793页

作者： Buzdalov, Maxim Doerr, Benjamin Doerr, Carola Vinokurov, Dmitry ITMO Univ St Petersburg Russia Inst Polytech Paris Lab Informat LIX Ecole Polytech CNRS Palaiseau France Sorbonne Univ LIP6 CNRS Paris France

Runtime analysis aims at contributing to our understanding of evolutionary algorithms through mathematical analyses of their runtimes. In the context of discrete optimization problems, runtime analysis classically studies the time needed to find an optimal solution. However, both from a practical and from a theoretical viewpoint, more fine-grained performance measures are needed to gain a more detailed understanding of the main working principles and their resulting performance implications. Two complementary approaches have been suggested: fixed-budget analyses and fixed-target analyses. In this work, we conduct an in-depth study on the advantages and the limitations of fixed-target analyses. We show that, different from fixed-budget analyses, many classical methods from the runtime analysis of discrete evolutionary algorithms yield fixed-target results without greater effort. We use this to conduct a number of new fixed-target analyses. However, we also point out examples where an extension of existing runtime results to fixed-target results is highly non-trivial.

关键词： Runtime analysis evolutionary computation Randomized search heuristics

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