基于混合变异的萤火虫群优化算法

摘要

While basic glowworm swarm optimisation (GSO)is applied to solving the problem of global optimisation of functions,it has the problems of slow convergence in later period and being prone to falling into local optimum.Therefore we put forward a hybrid mutation-based algorithm of glowworm swarm optimisation.The algorithm uses chaotic mutation and boundary mutation to improve the diversity of the population,and prevents the algorithm from falling into local optimum;moreover this enables the algorithm to achieve a more accurate solution.Six standard test functions are applied to the test,results show that the improved glowworm swarm optimisation is better than the basic GSO in terms of the optimisation speed and precision and the convergence rate.%基本萤火虫群优化 GSO（Glowworm Swarm Optimization）算法在求解函数全局寻优问题时，存在后期收敛速度慢、容易陷入局部极值等问题。为此，提出一种基于混合变异的萤火虫群优化算法。该算法用混沌变异和边界变异来增加种群的多样性，避免算法陷入局部最优，且能使算法获得精度更高的解。运用六个标准测试函数进行测试，结果表明，改进后的萤火虫群优化算法比基本 GSO 算法具有更高的寻优速度、寻优精度和收敛率。