A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems

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• 作者：Kaveh Khalili-Damghani^a ; ^{kaveh.khalili@gmail.com} ; Amir-Reza Abtahi¹ ; ^b ; ^{amir_abtahi@yahoo.com} ; Madjid Tavana² ; ^c ; ^{tavana@lasalle.edu}
• 关键词：ANOVA ; analysis of variance ; AUGMECON ; efficient epsilon-constraint method ; CI ; confidence intervals ; cTV-MOPSO ; customized time-variant multi-objective particle swarm optimization ; DM ; decision maker ; DiM ; diversification metric ; DSAMOPSO ; dynamic self-a
• 刊名：Reliability Engineering and System Safety
• 出版年：2013
• 出版时间：March, 2013
• 年：2013
• 卷：111
• 期：Complete
• 页码：58-75
• 全文大小：931 K

文摘

In this paper, a new dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO) method is proposed to solve binary-state multi-objective reliability redundancy allocation problems (MORAPs). A combination of penalty function and modification strategies is used to handle the constraints in the MORAPs. A dynamic self-adaptive penalty function strategy is utilized to handle the constraints. A heuristic cost-benefit ratio is also supplied to modify the structure of violated swarms. An adaptive survey is conducted using several test problems to illustrate the performance of the proposed DSAMOPSO method. An efficient version of the epsilon-constraint (AUGMECON) method, a modified non-dominated sorting genetic algorithm (NSGA-II) method, and a customized time-variant multi-objective particle swarm optimization (cTV-MOPSO) method are used to generate non-dominated solutions for the test problems. Several properties of the DSAMOPSO method, such as fast-ranking, evolutionary-based operators, elitism, crowding distance, dynamic parameter tuning, and tournament global best selection, improved the best known solutions of the benchmark cases of the MORAP. Moreover, different accuracy and diversity metrics illustrated the relative preference of the DSAMOPSO method over the competing approaches in the literature.