Initial Populations with a Few Heuristic Solutions Significantly Improve Evolutionary Multi-Objective Combinatorial Optimization-Reference-Cited by-同舟云学术

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

Published:2023-12-05 Issue: Volume:12 Page:1398-1405
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Container-title:2023 IEEE Symposium Series on Computational Intelligence (SSCI)
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Author:

Gong Cheng¹,Nan Yang¹,Pang Lie Meng¹,Ishibuchi Hisao¹,Zhang Qingfu²

Affiliation:

1. Southern University of Science and Technology,Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Department of Computer Science and Engineering,Shenzhen,China,518055

2. City University of Hong Kong,Department of Computer Science,Kowloon Tong,Hong Kong

Funder

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10371778/10371788/10371937.pdf?arnumber=10371937

Reference24 articles.

1. Multi-objective combinatorial optimization problems: A survey

2. A study of global convexity for a multiple objective travelling salesman problem;Ribeiro;Essays and Surveys in Metaheuristics,2002

3. Multi-objective vehicle routing problems

4. An efficient procedure for finding best compromise solutions to the multi-objective assignment problem

5. Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems

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