Initial Populations with a Few Heuristic Solutions Significantly Improve Evolutionary Multi-Objective Combinatorial Optimization

Cheng Gong1; Yang Nan1; Lie Meng Pang1; Hisao Ishibuchi1; Qingfu Zhang2

10.1109/SSCI52147.2023.10371937

2023

2770-0097

978-1-6654-3064-7

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

1398-1405

5-8 Dec. 2023

Mexico City, Mexico

Population initialization is a crucial and essential step in evolutionary multi-objective optimization (EMO) algorithms. The quality of the generated initial population can significantly affect the performance of an EMO algorithm. However, few studies have focused on designing a generalized initialization method to improve the performance of EMO algorithms in solving multi-objective combinatorial optimization (MOCO) problems. Most of the existing advanced initialization methods involve complex techniques tailored to the specific characteristics of the problems to be solved. In this paper, we propose a general and effective framework of population initialization for EMO algorithms, aiming to improve their performances in solving various MOCO problems. Our approach involves the inclusion of a few specific heuristic solutions, including extreme solutions and a center solution, into the initial population. This inclusion serves to guide the evolution of the population throughout the optimization process. Our experimental results show that initial populations with a few heuristic solutions significantly improve the performance of EMO algorithms. Algorithm behavior analysis and further study are also provided, allowing for a comprehensive understanding of the effectiveness and applicability of our proposed method.

Population initialization Evolutionary multi-objective optimization algorithms Multi-objective combinatorial optimization

第一

EI

20240415441860

IEEE

1.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
2.Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

Cheng Gong,Yang Nan,Lie Meng Pang,et al. Initial Populations with a Few Heuristic Solutions Significantly Improve Evolutionary Multi-Objective Combinatorial Optimization[C],2023:1398-1405.