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50th CIRP conference on Manufacturing Systems

作者： Tanimizu, Yoshitaka Sakamoto, Michisuke Nonomiya, Hideyuki Osaka Prefecture Univ Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

In recent years, many used products are returned from customers through reverse supply chains in order to be reused and recycled. Therefore, it is necessary to provide suitable disassembly procedures from the view point of economic efficiency. This research proposes a scheduling method for open-shop scheduling problems containing both disassembly operations and post-processing operations. A co-evolutionary algorithm is proposed to modify not only the sequences of disassembly and post-processing operations of products but also the sequences of products loaded on disassembly and post-processing machines. Two different kinds of individuals are modeled for both the sequences of operations and the sequences of products in the co-evolutionary algorithm. These individuals alternately repeat evolution at short time intervals, and affect their fitness values each other. We developed a prototype of scheduling system and applied it to some computational experiments for open-shop type scheduling problems including disassembly and post-processing operations. The experimental results of the proposed method were compared with the one of another scheduling method with various heuristic rules in order to evaluate the effectiveness of the proposed method. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Disassembly scheduling co-evolutionary algorithm Make-span

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A Method of coA Based on Multi-Agent co-evolutionary algorithm

A Method of COA Based on Multi-Agent Co-evolutionary Algorit...

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7th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Parallel Processing of Images and Optimization and Medical Imaging Processing

作者： Yu, Xin Dong, Shuaijun Wang, Hui Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819485793

In the complex situation of the battlefield, coA (course of action) places an important role. It is required to coordinate many resources in some actions to achieve the desired purpose in the battle. The main goal of coA is to arrange the action in the right order and to put the right resource in the right action. The task which is composed of many actions is always extremely complex. Therefore, coA is actually NP-Hard and a multi-objective optimization problem. It is difficult to solve this problem by common methods. In this paper, a mechanism of co-evolutionary is introduced to solve the problem of coA. It deals well with the problems of resource management and action scheduling.

关键词： coA agent co-evolutionary algorithm

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A co-evolutionary algorithm-Based Malware Adversarial Sample Generation Method 5

A Co-evolutionary Algorithm-Based Malware Adversarial Sample...

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5th IEEE conference on Dependable and Secure computing (IEEE DSC)

作者： Wang, Fangwei Lu, Yuanyuan Li, Qingru Wang, Changguang Bai, Yonglei Hebei Normal Univ Coll Comp & Cyber Secur Shijiazhuang Peoples R China Hebei Normal Univ Deans Off Shijiazhuang Peoples R China

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

The study of adversarial attacks on malicious code detection models will help identify and improve the flaws of detection models, improve the detection ability of adversarial attacks, and enhance the security of AI (Artificial Intelligent) algorithm-based applications. To address the problems of low efficiency, long time, and low evasion rate in generating adversarial samples, we propose a co-evolutionary algorithm-based adversarial sample generation method. We decompose the adversarial sample generation problem into three sub-problems, which are minimizing the number of modification actions, injecting less content, and being detected as benign by the target model. The two sub-problems of injecting less content and being detected as benign by the target model can be completed by minimizing the fitness function through the cooperation of two populations in coevolution. Minimizing the number of actions is achieved by a selection operation in the evolutionary process. We perform attack experiments on static malicious detection models and commercial detection engines. The experimental results show the generated adversarial samples can improve the evasion rate of some detection engines while ensuring the minimum number of modification actions and injecting less content. On the two static malicious detection models, our approach achieves more than an 80% evasion rate with fewer modification actions and injected content. The evasion rate on three commercial detection engines can reach 58.9%. Uploading the generated adversarial samples to the VirusTotal platform can evade an average of 54.0% of the anti-virus programs on the platform. Our approach is also compared with the adversarial attack approach based on an evolutionary algorithm to verify the necessity of minimizing the number of modification actions and injecting less content in adversarial sample generation.

关键词： adversarial attack co-evolutionary algorithm neural network adversarial sample

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Multi-guided population co-evolutionary algorithm based on multiple similarity decomposition for large-scale flexible job shop scheduling problem

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APPLIED SOFT coMPUTING 2024年 166卷

作者： Wang, cong Wei, Lixin Sun, Hao Hu, Ziyu Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligent Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

As manufacturing shifts towards large-scale production, the size of the workshop increases, and its search space exponentially expands. It is difficult for existing algorithms to obtain an ideal scheduling solution in an acceptable time. For the large-scale flexible job shop scheduling problem (LSFJSP), a multi-guided population co-evolutionary algorithm based on multiple similarity decomposition (MPCSD) is designed. Faced with the problem of high dimensionality and complex solution space, a multiple similarity decomposition strategy is proposed. It proceeds to group based on similarity information at the dimension and population level. To obtain convergence-preferred and diversity-preferred dimension groupings, a training-set solution selection method is proposed. Inspired by the idea of divide-and-conquer, a multi-guided co-evolutionary strategy is proposed. It improves the exploration efficiency of the algorithm in the search space. To test effectiveness on more complex LSFJSP, a set of large-scale test problems including LS1-12 are designed. On LS1-12, MPCSD is compared with seven other algorithms to demonstrate its superiority. MPCSD performed best on 11, 7, and 10 of the 12 test problems on Inverted Generational Distance (IGD), Hypervolume (HV) and Schott's Spacing Metric (SP), respectively. Meanwhile, the Relative Deviation (RD) results showed that MPCSD obtained the best fitness performance.

关键词： Large-scale flexible job shop scheduling Multi-objective problem Similarity decomposition co-evolutionary algorithm

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Game theory based co-evolutionary algorithm (GCEA) for solving multiobjective optimization problems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2004年第10期E87D卷 2419-2425页

作者： Sim, KB Kim, JY Lee, DW Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

When we try to solve Multiobjective Optimization Problems (MOPs) using an evolutionary algorithm, the Pareto Genetic algorithm (Pareto GA) introduced by Goldberg in 1989 has now become a sort of standard. After the first introduction, this approach was further developed and lead to many applications. All of these approaches are based on Pareto ranking and use the fitness sharing function to maintain diversity. On the other hand in the early 50's another scheme was presented by Nash. This approach introduced the notion of Nash Equilibrium and aimed at solving optimization problems having multiobjective functions that are originated from Game Theory and Economics. Since the concept of Nash Equilibrium as a solution of these problems was introduced, game theorists have attempted to formalize aspects of the equilibrium solution. The Nash Genetic algorithm (Nash GA), which is introduced by Sefrioui, is the idea to bring together genetic algorithms and Nash strategy. The aim of this algorithm is to find the Nash Equilibrium of MOPs through the genetic process. Another central achievement of evolutionary game theory is the introduction of a method by which agents can play optimal strategies in the absence of rationality. Not the rationality but through the process of Darwinian selection, a population of agents can evolve to an evolutionary Stable Strategy (ESS) introduced by Maynard Smith in 1982. In this paper, we propose Game theory based co-evolutionary algorithm (GCEA) and try to find the ESS as a solution of MOPs. By applying newly designed co-evolutionary algorithm to several MOPs, the first we will confirm that evolutionary game can be embodied by co-evolutionary algorithm and this co-evolutionary algorithm can find ESSs as a solutions of MOPs. The second, we show optimization performance of GCEA by applying this model to several test MOPs and comparing with the solutions of previously introduced evolutionary optimization algorithms.

关键词： multiobjective optimization problems (MOPs) Pareto optimal set game theory evolutionary stable strategy (ESS) co-evolutionary algorithm

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A Novel Elitism co-evolutionary algorithm for Antagonistic Weapon-Target Assignment

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IEEE ACCESS 2021年 9卷 139668-139684页

作者： Huang, Jichuan Li, Xiaoyang Yang, Zhen Kong, Weiren Zhao, Yiyang Zhou, Deyun AVIC Chengdu Aircraft Ind Grp Co Ltd Chengdu 610091 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China

The Antagonistic Weapon-Target Assignment (AGWTA) problem is a crucial decision issue in command & control (C2). Since this is a minimax problem, co-evolutionary algorithms can be used to solve it effectively. However, the co-evolutionary algorithm is originally designed for continuous minimax problems which loses its efficiency to discrete contexts. In this paper, a novel elitism co-evolutionary algorithm is proposed to solve the AGWTA. Firstly, an improved AGWTA model for air combat based on the attack and evasion strategies is proposed. Secondly, an elite cooperative genetic algorithm based on the framework of the co-evolutionary algorithm is put forward. In this proposed algorithm, a problem-specific coding method and evolution operator are designed. Meanwhile, an elite individual update mechanism is presented. Finally, based on the analysis of the relationship between the feasible solutions under the air combat environment, an evaluation index is proposed. Experiments show that the proposed algorithm has higher accuracy than traditional co-evolutionary algorithms for solving AGWTA problems.

关键词： Aircraft Weapons Atmospheric modeling evolutionary computation Aerospace electronics Missiles Genetic algorithms command & control antagonistic weapon-target assignment evolutionary algorithm co-evolutionary algorithm

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A co-evolutionary algorithm for Open-shop Scheduling with Disassembly Operations

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Procedia CIRP 2017年 63卷 289-294页

作者： Yoshitaka Tanimizu Michisuke Sakamoto Hideyuki Nonomiya Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai-shi Osaka 599-8531 Japan

关键词： Disassembly scheduling co-evolutionary algorithm Make-span

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Mixed-traffic Signal Timing at Signalized Intersection Based on Adaptive co-evolutionary algorithm

Mixed-traffic Signal Timing at Signalized Intersection Based...

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7th World congress on Intelligent control and Automation

作者： He, Ming Wang, Hui Zhejiang Univ Res Ctr Intelligent Transportat Syst State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781424421138

In order to solve the signal timing of the urban single-point intersection under the mixed-traffic, the shortest average delay were taken as objective, and the intersection geometry shape, channelization status and prohibition of traffic flow conflicts were taken as restrictions. A new global search algorithm combining adaptive genetic algorithm with co-evolutionary algorithm was put forward to solve the optimization problem, and simulation test with the actual data of Tiyuchang-Jianguo intersection indicates that the proposed algorithm overcomes the shortcomings such as local convergence, compared with traditional genetic algorithm;and it is feasible and effective for mixed-traffic signal timing at signalized intersection.

关键词： intersection mix traffic adaptive genetic algorithm co-evolutionary algorithm

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Signal Timing at T Intersection Based on Adaptive co-evolutionary algorithm

Signal Timing at T Intersection Based on Adaptive Co-evoluti...

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27th Chinese control conference

作者： He Ming Wang Hui Liu Hong Zhejiang Univ Res Ctr Intelligent Transportat Syst State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9787900719706

Based on the modified co-evolutionary genetic algorithm, the signal timing of the urban single-point T intersection was optimized. In order to solve this problem, the shortest average delay was taken as objective, and the intersection geometry shape and prohibition of traffic flow conflicts were taken as restrictions. Simulation experiments show that the optimization signal phase figure results are consistent with the actual traffic situation, and the adaptive co-evolutionary genetic algorithm, which is a new global search algorithm, is feasible and effective for mixed-traffic signal timing at T signalized intersection.

关键词： T Intersection Mix Traffic co-evolutionary algorithm Adaptive

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A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems

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NEUROcoMPUTING 2012年 98卷 76-89页

作者： Sun, Yang Zhang, Lingbo Gu, Xingsheng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Intelligent evolutionary algorithms have been widely used to solve large-scale, complex global optimization problems. co-evolutionary algorithm (CEA), cultural algorithm (CA), and particle swarm optimization (PSO) are all promising methods in the field of intelligent computation. In this paper, a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed. In CECBPSO, a novel space called shared global belief space (SGBS) is introduced into the co-evolutionary mechanism, and a new co-evolutionary cultural framework is built. Through the synergistic mechanism, the algorithm has higher probability of avoiding local optima and the whole swarm can find global optima more quickly. Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Extensive computational studies are also carried out to evaluate the performance of CECBPSO on thirteen benchmark functions and three real-life optimization problems. The results show that the proposed algorithm has superior performance to other compared algorithms in terms of accuracy and convergence speed, especially on high-dimensional problems. (c) 2012 Elsevier B.V. All rights reserved.

关键词： co-evolutionary algorithm Cultural algorithm Particle swarm optimization Global optimization Factorial design Orthogonal test

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