A hybrid multifactorial evolutionary algorithm and firefly algorithm for the clustered minimum routing cost tree problem

Highlights：

• Develop a new encoding and decoding scheme for the CluMRCT problem, allowing evolutionary algorithms to function and focus on more potential search areas on complete and sparse graphs.

• Propose a combination of the multifactorial evolutionary algorithm and firefly algorithm, which enhances the exploitation ability and the quality of knowledge transfers of the evolutionary multitasking algorithm when solving low-similarity tasks.

• Evaluate the efficiency of the proposed algorithm and encoding method on various instances. The results proved that the proposed method outperformed all existing approaches in terms of solution quality and convergence trend.

摘要

•Develop a new encoding and decoding scheme for the CluMRCT problem, allowing evolutionary algorithms to function and focus on more potential search areas on complete and sparse graphs.•Propose a combination of the multifactorial evolutionary algorithm and firefly algorithm, which enhances the exploitation ability and the quality of knowledge transfers of the evolutionary multitasking algorithm when solving low-similarity tasks.•Evaluate the efficiency of the proposed algorithm and encoding method on various instances. The results proved that the proposed method outperformed all existing approaches in terms of solution quality and convergence trend.

论文关键词：Multifactorial evolutionary algorithm,Firefly algorithm,Hybrid algorithm,Minimum routing cost,Clustered Tree Problem

论文评审过程：Received 19 August 2021, Revised 12 January 2022, Accepted 14 January 2022, Available online 25 January 2022, Version of Record 12 February 2022.