Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization

Easy convergence to a local optimum, rather than global optimum could unexpectedly happen in practical multimodal optimization problems due to interference phenomena among physically constrained dimensions. In this paper, an altering strategy for dynamic diversity Flower pollination algorithm (FPA) is proposed for solving the multimodal optimization problems. In this proposed method, the population is divided into several small groups. Agents in these groups are exchanged frequently the evolved fitness information by using their own best historical information and the dynamic switching probability is to provide the diversity of searching process. A set of the benchmark functions is used to test the quality performance of the proposed method. The experimental result of the proposed method shows the better performance in comparison with others methods.