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35th Chinese Control and Decision Conference (CCDC)

作者： Li, Chengshuai Han, Yuyan Wang, Yong Wang, Yuhang Zhang, Chenyao Yang, Feng Liaocheng Univ Sch Comp Sci Liaocheng Shandong Peoples R China Zhongtong Bus Holding Co Ltd Paint Shop Liaocheng Shandong Peoples R China

ISBN: (纸本)9798350334722

With the development of multi-variety and small-scale production models, the application of lot streaming technology in manufacturing is becoming increasingly widespread. The lot streaming technology can support continuous overlapping operations to increase productivity. This paper concentrates on studying the hybrid flowshop scheduling problem with consistent sublots (HFSP_CS) with the objective of minimizing the maximum completion time. To solve HFSP-CS, an effective cooperative coevolutionary algorithm (EVCCEA) is proposed by integrating the variable neighborhood descent strategy. In EVCCEA, the HFSP-CS is decomposed, a two -level coding strategy and a novel collaboration mechanism are developed. In addition, two enhanced neighborhood perturbation strategies arc proposed to find promising solutions. Finally, the validity of EVCCEA for solving HFSP-CS is proved through extensive experiments.

关键词： hybrid flowshop scheduling consistent sublots collaboration mechanism cooperative coevolutionary algorithm variable neighborhood descent

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An effective cooperative coevolutionary algorithm for hybrid flowshop scheduling with consistent sublots

An effective cooperative coevolutionary algorithm for hybrid...

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第35届中国控制与决策会议

作者： Chengshuai Li Yuyan Han Yong Wang Yuhang Wang Chenyao Zhang Feng Yang School of Computer Science Liaocheng University

With the development of multi-variety and smallscale production models,the application of lot streaming technology in manufacturing is becoming increasingly *** lot streaming technology can support continuous overlapping operations to increase *** paper concentrates on studying the hybrid flowshop scheduling problem with consistent sublots（HFSPS） with the objective of minimizing the maximum completion *** solve HFSP-CS,an effective cooperative coevolutionary algorithm（EVCCEA） is proposed by integrating the variable neighborhood descent *** EVCCEA,the HFSP-CS is decomposed,a two-level coding strategy and a novel collaboration mechanism are *** addition,two enhanced neighborhood perturbation strategies are proposed to find promising ***,the validity of EVCCEA for solving HFSP-CS is proved through extensive experiments.

关键词： hybrid flowshop scheduling consistent sublots collaboration mechanism cooperative coevolutionary algorithm variable neighborhood descent

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A cooperative coevolutionary algorithm for the Multi-Depot Vehicle Routing Problem

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 43卷 117-130页

作者： de Oliveira, Fernando Bernardes Enayatifar, Rasul Sadaei, Hossein Javedani Guimaraes, Frederico Gadelha Potvin, Jean-Yves Univ Fed Minas Gerais Grad Program Elect Engn BR-31270901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Elect Engn BR-31270901 Belo Horizonte MG Brazil Univ Fed Ouro Preto Dept Comp & Sistemas Joao Monlevade MG Brazil Univ Montreal CIRRELT Montreal PQ H3C 3J7 Canada

The Multi-Depot Vehicle Routing Problem (MDVRP) is an important variant of the classical Vehicle Routing Problem (VRP), where the customers can be served from a number of depots. This paper introduces a cooperative coevolutionary algorithm to minimize the total route cost of the MDVRP. coevolutionary algorithms are inspired by the simultaneous evolution process involving two or more species. In this approach, the problem is decomposed into smaller subproblems and individuals from different populations are combined to create a complete solution to the original problem. This paper presents a problem decomposition approach for the MDVRP in which each subproblem becomes a single depot VRP and evolves independently in its domain space. Customers are distributed among the depots based on their distance from the depots and their distance from their closest neighbor. A population is associated with each depot where the individuals represent partial solutions to the problem, that is, sets of routes over customers assigned to the corresponding depot. The fitness of a partial solution depends on its ability to cooperate with partial solutions from other populations to form a complete solution to the MDVRP. As the problem is decomposed and each part evolves separately, this approach is strongly suitable to parallel environments. Therefore, a parallel evolution strategy environment with a variable length genotype coupled with local search operators is proposed. A large number of experiments have been conducted to assess the performance of this approach. The results suggest that the proposed coevolutionary algorithm in a parallel environment is able to produce high-quality solutions to the MDVRP in low computational time. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Multi-Depot Vehicle Routing Problem Vehicle routing cooperative coevolutionary algorithm Evolution strategies

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A cooperative coevolutionary optimization design of urban transit network and operating frequencies

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 160卷 113736-113736页

作者： Liang, Mingzhang Wang, Wei Dong, Changyin Zhao, De Southeast Univ Sch Transportat Jiangsu Key Lab Urban Its Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing 210096 Peoples R China

The transit network design and frequency setting problem (TNDFSP) is a complex combinatorial optimization problem. Generally, the nature of multiobjective in TNDFSP has not attracted enough attention, and the frequency setting is directly embedded as a subproblem to generate a unique set of frequencies for a given transit network, ignoring trade-off solutions among multiple objectives with different sets of frequencies. In this study, the problem is formulated as a multiobjective model with two conflicting objectives of minimizing passengers' and operators' costs. Moreover, we establish two populations to simultaneously optimize networks and frequencies. Also a cooperative coevolutionary multiobjective evolutionary algorithm (CCMOEA) is developed to collaboratively coevolve these two populations along multiple objectives. Unsatisfied demand is embedded into the individual prioritization process, and infeasible individuals can be retained instead of being replaced arbitrarily, driving the evolution to gradually generate more feasible solutions. The proposed CCMOEA is tested on the well-known Mandl's benchmark. The results show that our algorithm can efficiently produce a comprehensive set of high quality trade-off solutions. These solutions perform well with lower waiting time, competitive in vehicle travel time and number of transfers, resulting in lower user costs than previously published results in the same fleet size. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Transit network design Frequency setting Public transportation Multiobjective optimization cooperative coevolutionary algorithm

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cooperatively coevolutionary optimization design of limited-stop services and operating frequencies for transit networks

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 125卷 103038-103038页

作者： Liang, Mingzhang Zhang, H. M. Ma, Rui Wang, Wei Dong, Changyin Southeast Univ Sch Transportat Jiangsu Key Lab Urban ITS Nanjing 210096 Peoples R China Southeast Univ Jiangsu Prov Collaborat Innovat Ctr Modern Urban Nanjing 210096 Peoples R China Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA Univ Alabama Dept Civil & Environm Engn Huntsville AL 35899 USA

The objective of the limited-stop service design and frequency setting problem (LSDFSP) is to realize the operation of a transit route with a set of elaborate service patterns and corresponding frequencies that can minimize the total social cost of the users and operators. In practice, these different patterns and frequencies involve a trade-off between the user and operator requirements, and this aspect cannot be fully clarified by a single-objective optimization problem. Therefore, in this study, the LSDFSP is considered at the network level and formulated as a multiobjective optimization problem with competing objectives of minimizing the user and operator costs. The cooperative coevolutionary multi-objective evolutionary algorithm is redesigned to collaboratively optimize the service patterns and frequencies. A prioritization method is proposed to separately incorporate different types of unsatisfied demand as critical indicators, prompting the algorithm to dig deeper into valuable genes and evolve more feasible solutions. The proposed algorithm is tested on a small network and a real intricate network. It is noted that higher frequencies increased the fleet size and decreased the users? waiting time and in-vehicle time. Furthermore, skipping more stations reduced the fleet size and users? in-vehicle time, while increasing the users? waiting time and number of transfers. The computational results indicated that the proposed algorithm can suitably incorporate the trade-offs and generate an accurate set of Pareto-optimal solutions.

关键词： Public transportation Limited-stop service design Frequency setting Multi-objective optimization cooperative coevolutionary algorithm

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A cooperative coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms 17

A Cooperative Coevolutionary Approach to Maximise Surveillan...

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IEEE 17th Annual Consumer Communications and Networking Conference (CCNC)

作者： Stolfi, Daniel H. Brust, Matthias R. Danoy, Gregoire Bouvry, Pascal Univ Luxembourg SnT Esch Sur Alzette Luxembourg Univ Luxembourg FSTC CSC Esch Sur Alzette Luxembourg

ISBN: (纸本)9781728138930

This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic algorithm (GA) and two cooperative coevolutionary Genetic algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently.

关键词： swarm robotics mobility model unmanned aerial vehicle genetic algorithm cooperative coevolutionary algorithm

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Multiple-Preys Pursuit based on Biquadratic Assignment Problem

Multiple-Preys Pursuit based on Biquadratic Assignment Probl...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Sun, Lijun Lyu, Chao Shi, Yuhui Lin, Chin-Teng Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia Harbin Inst Technol Harbin Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781728183923

The multiple-preys pursuit (MPP) is the adversarial game between predators and preys. If the capture of a prey is defined as that it cannot move anymore due to the surrounding of predators, there are two kinds of task allocations. One is about assigning which prey to which group of predators so that all preys can be captured. The other is about assigning which capturing position to which predator to encircle the prey simultaneously. In this paper, the MPP is modeled as a dynamic optimization problem and each its time step is solved in two stages. Firstly, the first kind of task allocation problem is modeled as the biquadratic assignment problem (BiQAP) and a MPP fitness function is proposed for the evaluation of such BiQAP task allocations. In this way, the MPP is transformed to several single-prey pursuit (SPP) problems. Secondly, for each SPP, we extend the coordinated SPP strategy CCPSO-R (cooperative coevolutionary particle swarm optimization for robots) to its parallel version as PCCPSO-R to enable the parallel implicit capturing position allocating by parallel observation, decision making, and moving of predators. Through experiments of the current BiQAP solvers on the task allocation, we improve the best one of them in statistic based on the domain knowledge. Moreover, the advantages of PCCPSO-R in the capturing efficiency over CCPSO-R is testified in the MPP experiments.

关键词： multiple-preys pursuit biquadratic assignment problem (BiQAP) cooperative coevolutionary algorithm

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ARCHIVE-SHARED cooperative coevolutionary algorithm USING NASH EQUILIBRIA PRESERVATION

ARCHIVE-SHARED COOPERATIVE COEVOLUTIONARY ALGORITHM USING NA...

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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Chen, Haoyang Mori, Yasukuni Matsuba, Ikuo Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan

ISBN: (纸本)9781467318556;9781467318570

cooperative coevolutionary algorithm (CCEA) has been widely applied to solve large scale and complex problems, which result in significant speedups over traditional genetic algorithm. However, CCEA do not search for the optimal solutions but the compromised solutions because of its nature of cooperation. So in the case that the object problem has infinite Nash equilibria, CCEA is expected to converge into suboptimal solution even employing the shared archive. In this paper, we propose a Nash equilibria preservation strategy which enables CCEA to jump out of the suboptimal solution and to keep searching without falling into the same suboptimal solutions found so far. The experiment results show that the capability of the archive-shared CCEA has been improved by using the proposed strategy, especially in dealing with problem having infinite Nash equilibria.

关键词： cooperative coevolutionary algorithm Nash equilibria Function optimization Convergence

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ARCHIVE-SHARED cooperative coevolutionary algorithm USING NASH EQUILIBRIA PRESERVATION

ARCHIVE-SHARED COOPERATIVE COEVOLUTIONARY ALGORITHM USING NA...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： HaoyangChen YasukuniMori IkuoMatsuba Graduate School of Advanced Integration Science Chiba University

cooperative coevolutionary algorithm （CCEA） has been widely applied to solve large scale and complex problems,which result in significant speedups over traditional genetic ***,CCEA do not search for the optimal solutions but the compromised solutions because of its nature of *** in the case that the object problem has infinite Nash equilibria,CCEA is expected to converge into suboptimal solution even employing the shared *** this paper,we propose a Nash equilibria preservation strategy which enables CCEA to jump out of the suboptimal solution and to keep searching without falling into the same suboptimal solutions found so *** experiment results show that the capability of the archive-shared CCEA has been improved by using the proposed strategy,especially in dealing with problem having infinite Nash equilibria.

关键词： cooperative coevolutionary algorithm Nash equilibria Function optimization Convergence

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Genetic Programming-Based Code Generation for Arduino

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第11期11卷 538-549页

作者： Ferrel, Wildor Alfaro, Luis Univ Nacl San Agustin Arequipa Dept Acad Ingn Elect Arequipa Peru Univ Nacl San Agustin Arequipa Dept Acad Ingn Sistemas Arequipa Peru

This article describes a methodology for writing the program for the Arduino board using an automatic generator of assembly language routines that works based on a cooperative coevolutionary multi-objective linear genetic programming algorithm. The methodology is described in an illustrative example that consists of the development of the program for a digital thermometer organized on a circuit formed by the Arduino Mega board, a text LCD module, and a temperature sensor. The automatic generation of a routine starts with an input-output table that can be created in a spreadsheet. The following routines have been automatically generated: initialization routine for the text LCD screen, routine for determining the temperature value, routine for converting natural binary code into unpacked two-digit BCD code, routine for displaying a symbol on the LCD screen. The application of this methodology requires basic knowledge of the assembly programming language for writing the main program and some initial configuration routines. With the application of this methodology in the illustrative example, 27% of the program lines were written manually, while the remaining 73% were generated automatically. The program, produced with the application of this methodology, preserves the advantage of assembly language programs of generating machine code much smaller than that generated by using the Arduino programming language.

关键词： Genetic programming Arduino mega board multi-objective linear genetic programming cooperative coevolutionary algorithm automatic generation of programs Arduino based thermometer

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