A Hybrid Group Search Optimization Based on Fish Swarms

Group Search Optimization (GSO) is a Swarm Intelligence (SI) approach for continuous optimization problems inspired by animal searching behavior and group living theory. The Artificial Fish Swarm (AFS) is an intelligent optimization algorithm based on the behavior of fish. In this paper, a new hybrid group search optimization method is presented, using the behaviors of the fish as scrounging strategies. Eight benchmark functions are used to evaluate the performance of the proposed technique. Experimental results show that the proposed approach is able to achieve better results than standard GSO in most of the tested problems.