Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior

The fish swarm algorithm (FSA) is a new intelligent swarm modeling approach that consists primarily of searching, swarming, and following behaviors. This paper proposes several improvements of the FSA, including: (1) using particle swarm optimization formulation to reformulate the FSA, (2) integrating communication behavior into FSA, and (3) creating formulas for major FSA parameters. This paper also focuses on studying the effects of FSA behaviors on optimization during the evolution process. Results focus on the two case study categories of function optimization (eight benchmark functions) and neural network learning (single-input single-output system identification, multi-inputs single output system identification and Iris classification problem). Evidence indicates that the proposed FSA approach reduces the effort necessary to set parameters and that the proposed communication behavior indeed improves FSA.