An improved simplified swarm optimization

Abstract

Abstract This paper introduces an improved simplified swarm optimization (iSSO) by undertaking a major revision of the update mechanism (UM) of traditional SSO. To test its performance, the proposed iSSO is compared with another recently introduced swarm-based algorithm, the Artificial Bee Colony Algorithm (ABC), on 50 different widely used multivariable and multimodal numerical test functions. Numerical examples conclude that the proposed iSSO outperforms ABC in both solution quality and efficiency. We also test the roles of the proposed UMs and iterative local search. The proposed algorithm is thus useful to both practitioners and researchers.