Adaptive Mutation Dynamic Search Fireworks Algorithm

Abstract

Abstract:The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving optimization problems. However, dynFWA easily falls into local optimal solutions prematurely and it also has a slow convergence rate. In order to improve these problems, an adaptive mutation dynamic search fireworks algorithm (AMdynFWA) is introduced in this paper. The proposed algorithm applies the Gaussian mutation or the Levy mutation for the core firework (CF) with mutation probability. Our simulation compares the proposed algorithm with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.

Dynamic elite strategy mayfly algorithm

“…Usually, the time complexity of an algorithm is related to the specific operations such as addition, subtraction, multiplication, and division of the algorithm [46]. Assuming that the number of mayflies in the DESMA algorithm is N, the number of progeny mayflies is M, and the number of elite mayflies is k, the time complexity is analyzed according to the execution steps of the algorithm.…”