An Improved Slap Swarm Algorithm Incorporating Tent Chaotic Mapping and Decay Factor

Abstract

Aiming at the defects of local stagnation and slow convergence speed of Salp Swarm Algorithm in the optimization process, an improved Salp Swarm Algorithm (ISSA) incorporating Tent chaotic mapping and decay factor is proposed. To begin, the Tent chaotic mapping strategy is used to initialize the population, which allows the population to traverse the search space and improve the first stage of the algorithm's iteration speed; second, the decay factor is added to the leader position update, which expands the search range and improves the algorithm's search accuracy. The experimental results reveal that the improved algorithm's exploration ability and optimization accuracy have been greatly enhanced by a series of benchmark function simulations.