针对混合变量优化问题的协同进化蚁群优化算法

Coevolutionary ant colony optimization algorithm for mixed-variable optimization problem

针对由连续变量和分类变量构成的混合变量优化问题(MVOP),采用协同进化策略来对混合变量决策空间进行搜索,提出了一种协同进化蚁群优化算法(CACOAMV)。CACOAMV分别采用连续和离散蚁群优化(ACO)策略生成连续和分类变量子种群,通过合作者来对连续和分类变量子向量进行评价,分别对连续和分类变量子种群进行更新来实现对混合变量决策空间的高效协同搜索。进一步地,利用信息素平滑机制增强对分类变量解空间的全局探索能力,并设计了一种面向协同进化框架的"最佳+随机合作者"的重启策略来提高协同搜索效率。与混合变量的蚁群(ACOMV)算法和种群规模线性变小的差分进化-蚁群混合变量优化算法(L-SHADEACO)的比较表明,CACOAMV能够进行更有效的局部开发,从而提高最终结果在目标空间中的近似精度;与基于集合的混合变量差分进化算法(DEMV)相比较,CACOAMV能够在决策空间中更好地逼近全局最优解,具有更好的全局探索能力。综上,采用协同进化机制的CACOAMV能有效保持全局探索和局部开发的平衡,从而具有更好的寻优性能。

For Mixed-Variable Optimization Problem(MVOP)containing both continuous and categorical variables,a coevolution strategy was proposed to search the mixed-variable decision space,and a Coevolutionary Ant Colony Optimization Algorithm for MVOP(CACOAMV)was developed.In CACOAMV,the continuous and categorical subpopulations were generated by using the continuous and discrete Ant Colony Optimization(ACO)strategies respectively,the sub-vectors of continuous and categorical variables were evaluated with the help of cooperators,and the continuous and categorical sub-populations were respectively updated to realize the efficient coevolutionary search in the mixed-variable decision space.Furthermore,the ability of global exploration to the categorical variable solution space was improved by introducing a smoothing mechanism of pheromone,and a"best+random cooperators"restart strategy facing the coevolution framework was proposed to enhance the efficiency of coevolutionary search.By comparing with the Mixed-Variable Ant Colony Optimization(ACOMV)algorithm and the Success History-based Adaptive Differential Evolution algorithm with linear population size reduction and Ant Colony Optimization(L-SHADEACO),it is demonstrated that CACOAMVis able to perform better local exploitation,so as to improve approximation quality of the final results in the target space;the comparison with the set-based Differential Evolution algorithm with Mixed-Variables(DEMV)shows that CACOAMVis able to better approximate the global optimal solutions in the decision space and has better global exploration ability.In conclusion,CACOAMVwith the coevolutionary strategy can keep a balance between global exploration and local exploitation,which results in better optimization ability.