Multi‐objective Firefly algorithm combining logistic mapping and Cauchy mutation

Abstract

SummaryIn the process of evolution, the multi‐target Firefly algorithm has a single learning strategy and is easy to fall into premature convergence, resulting in poor comprehensive performance of the algorithm. To solve this problem, a multi‐objective firefly algorithm (MOFA‐LM) combining logistic mapping and Cauchy mutation is proposed in this article. To improve the uneven distribution of the initial population, the initial population with good ergodicity is generated by logistic mapping. Aiming at the problem that the population is easy to fall into local optimal, Cauchy mutation can generate relatively large disturbance when fireflies are updating their positions, which makes it easier to jump out of the local optimal value with a wide range of individual optimization, effectively improve the optimization accuracy of the algorithm, overcome the premature convergence of the algorithm and maintain the convergence of the population. In the experimental part, the proposed algorithm is compared with some multi‐objective optimization algorithms in 19 benchmark test functions, and the effectiveness of the strategy added by MOFA‐LM is verified. The results show that MOFA‐LM has obvious advantages over other algorithms in improving population convergence and distribution.