引入混沌干扰机制的变异天牛群搜索算法

A Mutation Beetle Swarm Optimization Algorithm with Chaotic Interference Mechanism

针对天牛须搜索(Beetle Antennae Search,BAS)算法收敛慢、精度低且容易陷入局部最优等缺陷,提出了一种引入混沌干扰机制的变异天牛群搜索算法(Chaotic Interference Mechanism of Mutation Beetle Swarm Optimization Algorithm,CMBSOA)。首先,应用粒子群(Particle Swarm Optimization,PSO)策略将天牛须搜索算法中的天牛个体扩展为天牛群,扩大算法的搜索范围,提高算法的全局搜索能力;其次,引入Logistic混沌映射机制对天牛群进行混沌扰动,使初始化的种群以随机方式均匀分布,以加快算法的收敛速度;最后,提出变异因子策略进行位置更新,使该算法更易跳出局部最优,增强算法的稳定性与精度。为了验证CMBSOA算法的有效性,将其与天牛群算法(Beetle Swarm Antennae Search,BSAS)及PSO通过2组单峰和3组多峰测试函数进行测试和对比,结果表明,CMBSOA算法具有较强的稳定性,此外还具有更优的精度和较快的收敛速度,且能最大限度地避免产生局部最优解问题。

In order to address the defects of BAS(Beetle Antennae Search)algorithm such as slow convergence,low precision and easy to fall into local optimum,a CMBSOA(Chaotic Interference Mechanism of Mutation Beetle Swarm Optimization)algorithm is proposed.First,the PSO(Particle Swarm Optimization)is used to extend the individual beetles in the BAS algorithm to a beetle swarm,expanding the search range of the algorithm and improving its global search capability.Then,the logistic chaos mapping mechanism is introduced to chaos the beetle swarm so that the initialized population is evenly distributed in a random manner,thus the convergence speed of the algorithm is accelerated.Finally,a mutation factor strategy is added to update the position,making the algorithm easier to jump out of the local optimum to enhance the stability and accuracy of the algorithm.In order to verify the validity of CMBSOA,it is tested and compared with the BSAS(Beetle Swarm Antennae Search)algorithm and PSO through two-group single-peak and three-group multi-peak test functions.The results indicate that the CMBSOA algorithm has strong stability,in addition to better accuracy and faster convergence speed,and can avoid the problem of local optimal solutions to the greatest extent.