Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems

doi:10.1016/j.asoc.2022.108634

Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems

2022-05-01

34

PRE

AI

DOI：10.1016/j.asoc.2022.108634

Original

Original request for help

Save

Abstract

En 中文

Nowadays, more and more optimization techniques are used to deal with complex engineering optimization problems. Firefly algorithm (FA) inspired by the flash communication between fireflies, has been proven to be competitive with other swarm intelligence algorithms and has been widely applied to solve complex engineering optimization problems. However, FA has some defects in dealing with complex engineering optimization problems, such as the exploration and exploitation cannot be well balanced. Therefore, in order to achieve effective performance, the different characteristics of search strategies can be applied at different stages of the search process to achieve a balance between exploration and exploitation. In this paper, a multi-strategy firefly algorithm with selective ensemble (MSEFA) is proposed. In MSEFA, the algorithm has three novel search strategies with different characteristics in the strategy pool. In addition, an idea of selective ensemble is adopted to design a priority roulette selection method. The method can select suitable search strategies in different search stages and coordinate the balance of strategies so that better results can be obtained. Furthermore, a parameter adaptive transformation mechanism is designed to control the decreasing rate of step size alpha. To verify the effectiveness of MSEFA, performance tests are conducted on the CEC 2013 and CEC 2019 test suites, after which MSEFA is used to solve four complex engineering optimization problems. Experimental results show that MSEFA has the best performance compared with other FA variants and other improved swarm intelligence algorithms. In addition, MSEFA also achieves the best results in dealing with four complex engineering optimization problems. (c) 2022 Elsevier B.V. All rights reserved.

Keywords:

Swarm intelligence algorithm

Firefly algorithm

Multi-strategy

Selective ensemble

Complex engineering optimization

problems

Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems

Abstract

Journal

Organization