DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers

The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid in...

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Vydáno v:	Frontiers of information technology & electronic engineering Ročník 23; číslo 11; s. 1714 - 1732
Hlavní autoři:	Zhao, Luda, Wang, Bin, Jiang, Xiaoping, Lu, Yicheng, Hu, Yihua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hangzhou Zhejiang University Press 01.11.2022 Springer Nature B.V
Témata:	Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Electrical Engineering Electronics and Microelectronics Evolutionary algorithms Genetic algorithms Instrumentation Multiple objective analysis Networks Optimization Pareto optimization Preferences O224 种群更新策略多目标进化算法(MOEA) 决策者偏好形式化偏好交互
ISSN:	2095-9184, 2095-9230
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Abstract	The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.
AbstractList	The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers’ preference information region. Therefore, we propose a multi-objective optimization algorithm, referred to as the double-grid interactive preference based MOEA (DIP-MOEA), which explicitly takes the preferences of decision makers (DMs) into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preferences of DMs, the membership functions are mapped to generate a decision preference grid and a preference error grid. Then, we put forward two dominant modes of population, preference degree dominance and preference error dominance, and use this advantageous scheme to update the population in these two grids. Finally, the populations in these two grids are combined with the DMs’ preference interaction information, and the preference multi-objective optimization interaction is performed. To verify the performance of DIP-MOEA, we test it on two kinds of problems, i.e., the basic DTLZ series functions and the multi-objective knapsack problems, and compare it with several different popular preference-based MOEAs. Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs, quickly provides the test results, and has better performance in the distribution of the Pareto front solution set.
Author	Jiang, Xiaoping Lu, Yicheng Hu, Yihua Zhao, Luda Wang, Bin
Author_xml	– sequence: 1 givenname: Luda orcidid: 0000-0002-7476-5896 surname: Zhao fullname: Zhao, Luda email: zhaoluda@nudt.edu.cn organization: College of Electronic Engineering, National University of Defense Technology, Third Interdisciplinary Center, National University of Defense Technology – sequence: 2 givenname: Bin orcidid: 0000-0003-0593-3531 surname: Wang fullname: Wang, Bin email: wbeeinudt@126.com organization: College of Electronic Engineering, National University of Defense Technology, Third Interdisciplinary Center, National University of Defense Technology – sequence: 3 givenname: Xiaoping surname: Jiang fullname: Jiang, Xiaoping organization: College of Electronic Engineering, National University of Defense Technology, Third Interdisciplinary Center, National University of Defense Technology – sequence: 4 givenname: Yicheng surname: Lu fullname: Lu, Yicheng organization: Unit 78092 of People’s Liberation Army of China – sequence: 5 givenname: Yihua surname: Hu fullname: Hu, Yihua organization: College of Electronic Engineering, National University of Defense Technology, Third Interdisciplinary Center, National University of Defense Technology
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Snippet	The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms (MOEAs) lies a certain distance away from the...
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SubjectTerms	Communications Engineering Computer Hardware Computer Science Computer Systems Organization and Communication Networks Electrical Engineering Electronics and Microelectronics Evolutionary algorithms Genetic algorithms Instrumentation Multiple objective analysis Networks Optimization Pareto optimization Preferences
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Title	DIP-MOEA: a double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers
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