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A dynamic competitive swarm optimizer based-on entropy for large scale optimization

In this paper, a dynamic competitive swarm optimizer (DCSO) based on population entropy is proposed. The new algorithm is derived from the competitive swarm optimizer (CSO). The new algorithm uses population entropy to make a quantitative description about the diversity of population, and to divide the population into two sub-groups dynamically. During the early stage of the execution process, to speed up convergence of the algorithm, the sub-group with better fitness will have a small size, and worse large sub-group will learn from small one. During the late stage of the execution process, to keep the diversity of the population, the sub-group with better fitness will have a large size, and small worse sub-group will learn from large group. The proposed DCSO is evaluated on CEC'08 benchmark functions on large scale global optimization. The simulation results of the example indicate that the new algorithm has better and faster convergence speed than CSO.