Krill herd algorithm based on dynamic pressure control operator

doi:10.11772/j.issn.1001-9081.2018081661

Abstract

Abstract: Aiming at the problem that basic Krill Herd (KH) algorithm has poor local search ability and insufficient exploitation capacity on complex function optimization problems, a Krill Herd algorithm based on Dynamic Pressure Control operator (DPCKH) was proposed. A new dynamic pressure control operator was added to the basic krill herd algorithm, which made it more effective on complex function optimization problems. The dynamic pressure control operator quantified the induction effects of several different outstanding individuals on the target individual through Euclidean distance, accelerating the production of new krill individuals near the excellent individuals and improving the local exploration ability of krill individuals. Compared to ACO (Ant Colony Optimization) algorithm, DE algorithm, KH algorithm, KHLD (Krill Herd with Linear Decreasing step) algorithm and PSO (Particle Swarm Optimization) algorithm on 7 benchmark functions, DPCKH algorithm has stronger local exporatioin and exploitation ability.

Key words: Krill Herd (KH) algorithm, dynamic pressure control operator, function optimization, exploitation capacity, exploration capacity

摘要： 针对基础磷虾群（KH）算法在求解复杂函数优化问题时局部搜索能力差、求解精度低、收敛速度慢、容易陷入局部最优等问题，提出一种基于动态压力控制算子的磷虾群算法（DPCKH）。该算法将一种新的动态压力控制算子加入了标准磷虾群算法，使其处理复杂函数优化问题更有效。动态压力控制算子通过欧氏距离量化了多个不同优秀个体对目标个体的诱导效应，进而在优秀个体附近加速产生新磷虾个体，提高了磷虾个体的局部探索能力。通过比较蚁群算法（ACO）、差分进化算法（DE）、磷虾群算法（KH）、改进的磷虾群算法（KHLD）和粒子群算法（PSO），DPCKH算法在7个测试函数上的结果表明，DPCKH算法与ACO算法、DE算法、KH算法、KHLD算法和PSO算法相比有着更强的局部勘测能力，其开采能力更强。

关键词: 磷虾群算法, 动态压力控制算子, 函数优化, 开采能力, 探索能力

CLC Number:

TP18

SHEN Ying, HUANG Zhangcan, TAN Qing, LIU Ning. Krill herd algorithm based on dynamic pressure control operator[J]. Journal of Computer Applications, 2019, 39(3): 663-667.

沈莹, 黄樟灿, 谈庆, 刘宁. 基于动态压力控制算子的磷虾群算法[J]. 计算机应用, 2019, 39(3): 663-667.

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