基于自适应正态云模型的灰狼优化算法

灰狼优化算法(GWO)是一种模拟狼群等级制度和捕食行为的群体智能算法,存在收敛精度低、易陷入局部最优解等问题,为提高GWO的算法性能,提出一种基于Tent映射和正态云发生器的改进灰狼优化算法(CGWO).在灰狼群初始化阶段引入Tent映射,增加种群个体多样性以提高算法的优化效率;在攻击猎物阶段采用正态云模型对狼群位置进行更新,使算法前期具有较好的随机性和模糊性,提高全局开发能力,助其跳出局部最优解.随着迭代次数增加,自适应调整正态云模型熵值,使后期随机性和模糊性随之减小,有效改善局部开发能力,提高其收敛精度.选用20个通用的标准测试函数对CGWO算法性能进行验证,分别从单峰、多峰以及复合函数寻优结果与多种优化算法进行对比分析.结果表明,在同等测试条件下,CGWO算法寻优效率和收敛精度更高,能很快跳出局部最优解,在全局搜索和局部开发能力上更为平衡.

Grey wolf optimization algorithm based on adaptive normal cloud model

Grey wolf optimization (GWO) is a kind of swarm intelligence algorithm which simulates the rank system and predatory behavior of wolves. It has some shortcomings such as low convergence accuracy, easily falling into local optimal solution and so on. In order to improve the performance of the GWO algorithm, this paper proposes an improved gray wolf optimization(CGWO) algorithm based on the Tent mapping and normal cloud model. In the initial population stage, the algorithm employs the Tent mapping to make the population evenly distributed in the search space to improve the optimization efficiency. In the stage of attacking prey, the normal cloud model is used to update the location of the wolves, so that the algorithm has better randomness and fuzziness in the early stage, which improves the ability of global exploration and local optimal solution avoidance. In the later stage, the entropy of the normal cloud model is decreased with the increase of the number of iterations, hence, the randomness and fuzziness are reduced, which effectively improves the local exploitation ability and the convergence accuracy. 20 international standard test functions are selected to benchmark the performance of the CGWO algorithm, and the optimization results of unimodal, multi-modal and composite function are compared with various optimization algorithms. The results show that the CGWO algorithm is improved in convergence rate and accuracy, and has better balance between global exploration ability and local exploitation ability.