协同进化引力磷虾觅食算法

在对当前基本磷虾觅食算法的特性进行分析和研究后，针对基本磷虾觅食算法运行速度慢、全局收敛性不强等缺点，为提高磷虾觅食算法收敛性能，引入协同进化机制和引力算法思想，提出一种协同进化引力磷虾觅食算法（co-evolutionary gravitational krill herd algorithm，CGKH）。首先，为深入挖掘种群内部个体性能，将种群分为两个子种群进行协同竞争操作，提高种群整体竞争性能，同时将协同竞争后的种群划分为开采磷虾、跟随磷虾和侦察磷虾，并依据开采、跟随和侦察3个阶段进行协同进化，以提高种群局部开采能力；其次，借鉴引力算法基本思想，将磷虾个体觅食行为中的吸引度转化为邻域个体引力，确保个体向最优个体方向寻优；最后，为避免进化停滞和陷入局部极值，采用聚群和追尾行为对磷虾个体进行随机扰动，以提高种群后期个体多样性。对算法的收敛性能和漂移特性进行了分析，同时对算法进化能力进行了分析。利用同类型算法和不同类型算法进行了仿真对比分析，充分验证了所提出算法的优良性能。

Co-evolutionary Gravitational Krill Herd Algorithm

In order to overcome the drawbacks of basic krill herd algorithms, such as low convergence rate and poor convergence ability, a novel co-evolutionary gravitational krill herd algorithm was proposed in consider of cooperative evolution and gravitational search algorithms. First, the whole population was divided into two cooperative competitive populations to promote the population competition, and then the population was divided into three parts including employed krill herd, onlookers and scout krill herd. The population evolved according to three phases in order to enhance the population local exploitation ability. Second, the affinity for foraging motion was viewed as the gravitation inspired by gravitational search algorithm, which can make sure the direction of the search. In the last, in order to evade evolution stagnation and trap into local optimum, the physical diffusion was modified as the huddling behavior and following behavior to promote the population diversity. The trait of convergence and drift were analyzed, and the evolution performance was also analyzed in the paper. Simulation results of same and different kinds of algorithms demonstrated that the algorithm performs better than other algorithms.