基于认知多样性变异的鸡群算法协同优化异步实现

从小鸡更新公式、优化方式和基于认知多样性变异三方面改进鸡群算法。在小鸡位置更新过程中加入自我学习系数,并向所在种群公鸡学习,同时对未知空间进行探索；采用逆序协同优化异步实现策略提高算法解决更高维度问题的能力；充分利用个体认知多样性,使个体最优以一定概率发生变异,从而带领群体逃离局部最优,收敛到全局最优。Benchmark function测试表明,改进的鸡群算法优于其他优化算法。模型数据反演结果表明,该算法具有很强的全局搜索能力,反演精度较高,同时抗噪能力很强。

Asynchronous Collaborative Chicken Swarm Optimization with Mutation Based on Cognitive Diversity

The standard chicken swarm optimization is improved from the following three aspects:chick-update formula,optimization method and mutation based on cognitive diversity.Self-learning factor is added to chick-update formula.It is assumed that chicks learn from their own roosters respectively,and meanwhile the unknown space is explored.Asynchronous collaborative optimization strategy is adopted with inverted order to improve capacity of solving higher-dimensions problems.Self-cognitive diversity is taken full advantage to make sure the pbests mutate at a certain probability to lead the swarm to escape from the local optimum to converge to the global optimum.Benchmark function test indicates ICSO is better than other optimization algorithms.Model seismic data inversion shows strong global search ability,high precision and strong antinoise ability as well.