A novel information spread evolutionary algorithm applied to function optimization

Information spread mechanism (ISM) plays an essential role in evolutionary algorithms, forming different optimization methodologies. This paper briefly analyzes some existed ISMs and proposes a novel information spread evolutionary algorithm (NISEA). The algorithm uses a special ISM aiming at diffusing partial information of an individual to accelerate the improvement of the whole individual. Two mutation strategies are incorporated to enhance the population diversity and the selection operation is adopted to direct the evolution. Extensive experiments on 23 benchmark functions are taken to evaluate the performance of NISEA. The results are compared with those obtained by particle swarm optimization (PSO) and fast evolutionary programming (FEP), demonstrating the effectiveness and efficiency of the proposed algorithm.