基于精英高斯学习的改进鱼群粒子群混合算法

为提高算法在高维函数上的寻优性能，提出改进鱼群粒子群混合算法。该算法将鱼群算法全局搜索性能好与粒子群算法局部搜索性能强的优点相结合，在寻优初始阶段采用鱼群算法获得最优群体，在后期用粒子群算法实现精搜索。针对初始种群随意性大、分布不均的问题，通过均匀初始化，优化初始种群的分布; 并对算法全局搜索方向性差、效率低的问题，采用仿照蛙跳算法的分组方式对种群进行分组，同时对组内优秀个体和一般个体使用不同搜索策略，提高搜索的目的性和效率。引入改进的精英高斯学习，从而提升最终结果的精度。利用该算法对6 个标准函数寻优并与其他算法比较，结果表明，该算法的改进有效且性能优于其他算法。

Improved Hybrid Algorithm with Fish Swarm-Particle Swarm Optimization Based on Elite Gaussian Learning

In order to improve the searching performance of the algorithm in finding high-dimensional functions，an improved particle swarm optimization algorithm is proposed． This algorithm combines the good global search performance of AFSA ( Artificial Fish Swarm Algorithm) with the advantage of strong local search performance of PSO ( Particle Swarm Optimization) ． In initial period，AFSA was used to obtain the optimal population，and PSO was used to achieve the fine search in the later stage． In order to solve the problem of arbitrary initial population and uneven distribution，uniform initialization was used to optimize the distribution． Aimed at the poor global search direction and low efficiency of the algorithm，grouping strategy based on SFLA ( Shuffled Frog Leaping Algorithm) was adopted，and different search strategies for good individuals and other ordinary individuals in the group were used to improve the purpose and efficiency of search． Because PSO is prone to stagnation and results in low accuracy of the final result，the improved elite Gaussian learning was introduced to enhance the accuracy of the final result． The proposed algorithm is used to solve function optimization on six standard functions and compared with other algorithms，the results show the improvement is effective and superior to other algorithms．