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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Bibliographic Details
Published in:Frontiers of information technology & electronic engineering Vol. 23; no. 11; pp. 1714 - 1732
Main Authors: Zhao, Luda, Wang, Bin, Jiang, Xiaoping, Lu, Yicheng, Hu, Yihua
Format: Journal Article
Language:English
Published: Hangzhou Zhejiang University Press 01.11.2022
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2095-9184
2095-9230
DOI:10.1631/FITEE.2100508