An Immigration Strategy-based Spherical Search Algorithm

Abstract

The spherical search algorithm (SS) is a novel and competitive algorithm applied to real-world problems. However, the population of SS algorithm is divided equally, which requires a large number of computation resources for different problems. To alleviate the issues, we propose an immigration strategy-based spherical search algorithm, namely ISS. ISS adaptively selects individuals that are successfully updated in each generation and replaces the operator in the next iteration. The experiments were conducted on the 30 benchmark functions from the IEEE CEC2017. ISS is compared with SS to verify the effectiveness of the proposed adaptive immigration strategy. Additionally, the classical differential evolutionary algorithm (DE) and a state-of-the-art triple archive particle swarm optimization (TAPSO) are compared to test its performance further. The population diversity is analyzed to discuss the effect of ISS. The experimental results demonstrate that the proposed immigration strategy is quite effective, and ISS is significantly better than its peer's algorithms.