自适应差分变异的人工鱼群算法

Adaptive and Differential Mutation Artificial Fish Swarm Algorithm

针对人工鱼群算法存在的全局搜索能力欠缺,鲁棒性差及易陷入局部极值等不足,提出一种自适应差分变异的人工鱼群算法(ADMAFSA).首先,该算法采用自适应视野和步长策略,改善种群个体在较优区域的精细搜索能力,提升算法的寻优精度.其次,在人工鱼群的随机行为中引入反向学习机制,通过发掘潜在的寻优空间,提高算法的全局搜索性能,避免算法早熟收敛.最后,借鉴差分进化算法对质量较差的人工鱼进行变异操作,从而增加鱼群的多样性,降低算法陷入局部极值的可能性.为验证改进算法的性能,本文对6个基准测试函数和8个CEC2019函数进行仿真,与其他AFSA变体、新型智能算法进行对比,实验结果表明,ADMAFSA在寻优精度和鲁棒性方面均有所提高.最后,在齿轮系设计问题上,进一步证明了改进算法具有较好的优化效果.

The original artificial fish swarm algorithm(AFSA)has weak global search ability and poor robustness and is easy to fall into local extremum.Given these problems,an adaptive and differential mutation artificial fish swarm algorithm(ADMAFSA)is proposed.Firstly,it utilizes an adaptive vision field and step length strategy to improve the fine search ability of individuals in better areas of the population and enhance the optimization accuracy of the algorithm.Secondly,to explore potential better areas,the opposition-based learning mechanism is introduced into the random behavior of artificial fish swarms.Thereby,the algorithm can get better global searching ability and avoid premature convergence.Finally,inspired by the differential evolution algorithm,a mutation operation is applied to poorly performing artificial fish to increase the diversity of the fish swarm and reduce the possibility of the algorithm falling into the local extremum.To validate the performance of the improved algorithm,the proposed algorithm is tested with six benchmark test functions and eight CEC2019 functions.The experimental results indicate that,compared to other AFSA variants and novel intelligent algorithms,ADMAFSA demonstrates improvements in terms of optimization accuracy and robustness.Furthermore,in designing the train of gears,the optimization effectiveness of the improved algorithm is further proved.