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Frontiers of Information Technology & Electronic Engineering 

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


Author(s):  Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU

Affiliation(s):  College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; more

Corresponding email(s):  zhaoluda@nudt.edu.cn, wbeeinudt@126.com

Key Words:  Multi-objective evolutionary algorithm (MOEA); Formalizing preference of decision makers; Population renewal strategy; Preference interaction


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Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Yihua HU. DIP-MOEA: a preference-based multi-objective evolutionary algorithm for formalizing preferences of decision makers[J]. Frontiers of Information Technology & Electronic Engineering , 1998, -1(3): .

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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 multi-objective algorithm (DIP-MOEA), that explicitly takes the preference of decision makers into account. First, according to the optimization objective of the practical multi-objective optimization problems and the preference of decision makers (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 advantage scheme to update the population in the two grids. Finally, the populations in the 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 its performance on two kinds of DTLZ series functions, which are the basic MOEA test functions and the multi-objective knapsack problem with seven different popular preference-based MOEAs in four classes. A comparison of the experimental results shows that DIP-MOEA can express the preference information of DMs well and provide a solution set that meets the preference of DMs, but also can quickly provide the test results, and has better performance in the distribution of the Pareto front solution set.

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