关键词: 群体智能, 鸡群算法, 动态惯性策略, 惯性权重

Abstract: Considering the problem that the original chicken swarm optimization algorithm is easy to fall into local optimum and becomes premature convergence, a chicken swarm optimization of hybrid evolutionary searching strategy is proposed. In this algorithm, subgroup size and hierarchy order are determined by interspecific and intraspecific competition, and subgroup roles are dynamically updated by competitive breeding. The global optimal guidance strategy and dynamic inertia strategy are introduced into the evolution optimization of population. Individual learning in search of food is adjusted through dynamic inertia strategy and guided by the optimal individual of the subgroup and populations, in order to balance the relationship between the global search and local search. The simulated experimental results show that the proposed algorithm is able to effectively improve the convergence speed and convergence precision compared with particle swarm optimization and original chicken swarm optimization.

Key words: swarm intelligence, chicken swarm optimization, dynamic inertia strategy, inertia weight