4.7 Article

Runtime analysis of immune-inspired hypermutation operators in evolutionary multi-objective optimization

期刊

SWARM AND EVOLUTIONARY COMPUTATION
卷 65, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.swevo.2021.100934

关键词

Artificial immune system; Multi-objective optimization problem; Hypermutation operator; Runtime analysis

资金

  1. National Natural Science Foundation of China [61773410, yy2020bsky050]

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The study compared the expected runtime of a simple multi-objective evolutionary algorithm using different mutation operators, showing that immune-inspired hypermutation operators can always find the Pareto fronts in polynomial expected runtime and sometimes exponentially faster on certain problems. This analysis enhances understanding of immune-inspired hypermutation operators in solving multi-objective optimization problems and may be useful for designing efficient multi-objective evolutionary algorithms.
Immune systems inspired algorithms with hypermutation operators have achieved great success in solving real-world single-objective as well as multi-objective optimization problems. Compared to the application, however, the theoretical analysis, particularly on understanding immune-inspired hypermutation operators, is underdeveloped. The few existing theoretical studies mainly focused on single-objective optimization. In this paper, we present a theoretical study for the effectiveness of immune-inspired hypermutation operators in solving multi-objective optimization problems. We compare the expected runtime of a simple multi-objective evolutionary algorithm using four typical immune-inspired hypermutation operators and two classical mutation operators. The results on four bi-objective optimization problems widely used in theoretical analysis, namely LOTZ, COCZ, Plateau-MOP and PL, show that using immune-inspired hypermutation operators can always find the Pareto fronts in polynomial expected runtime, which is slower than the best known expected runtime of using classical mutation operators by at most a factor of n. Particularly, on Plateau-MOP and PL, using immune-inspired hypermutation operators can be exponentially faster. This runtime analysis can enhance the understanding of immune-inspired hypermutation operators on solving multi-objective optimization problems, and might be helpful for designing efficient multi-objective evolutionary algorithms in practice.

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