基于自然选择和随机扰动的改进磷虾群算法

Improved Krill Herd Algorithm Based on Natural Selection and Random Disturbance

针对磷虾群算法在求解高维复杂优化问题时容易陷入局部最优的缺点,提出一种基于自然选择和随机扰动的改进磷虾群算法.首先提出基于时变的非线性递减策略计算诱导权重和觅食权重,对磷虾群的诱导运动和觅食运动进行了改进;其次在产生新一代磷虾种群时加入随机扰动因子,并且借鉴自然选择中适者生存的进化机制提升磷虾种群中个体的质量,有效的提升了磷虾群算法的全局搜索和局部勘探能力.最后通过9个Benchmark标准测试函数的实验,将该算法与其他算法进行性能对比分析.实验表明,该算法能够有效地避免早熟收敛,在全局搜索和局部勘探能力上有着显著优势.

On the shortcoming of easily plunged into local optimum of krill herd algorithm （ KH ） in solving high dimensional complex optimization problem, an improved krill herd algorithm based on natural selection and random disturbance （ ANRKH ） is proposed. This algorithm firstly applies nonlinear decreasing strategy based on time of induced weight and foraging weight into KH, induced movement and foraging activity of krill herd is improved highly. And then random disturbance factor is added into the process of generating the new generation of krill herd population. And the evolution of the survival of the fittest in natural selection mechanism enhances the quality of the individuals in the krill herd population. Those steps can effectively balance the exploration and development ability of KH. Finally through the experiments of 9 Benchmark standard test functions, this proposed algorithm compares with other algorithms. Experimental results demonstrate that this proposed algorithm can effectively avoid premature convergence problem, the abilities of global search and local exploration have a significant advantage.