基于对立搜索和混沌变异的磷虾觅食优化算法

An improved krill herd algorithm based on oppositional searching and chaos mutation

针对磷虾觅食算法存在容易陷入局部极值、收敛速度慢的问题,提出一种新的改进算法.首先,给出启发式二次对立点的定义并证明其性能优势,进而构造一种启发式二次对立搜索算子,以加快算法的收敛速度,提高全局探索能力;然后,采用分段线性混沌映射(PWLCM)混沌函数构造一种变尺度混沌变异算子,以增强算法跳出局部极值的能力.仿真实验表明,所提出算法能有效避免陷入局部极值,在收敛速度和寻优精度上得到大幅改善.

An improved algorithm based on oppositional searching and chaos mutation is proposed in order to deal with the deficiencies of the traditional krill herd optimization algorithm,including poor ability for avoiding local optimum and low convergence rate.The definition of the heuristic quasi-oppositional point is given,and its outstanding performance is proven.Then,a heuristic quasi-oppositional searching operator is designed for accelerating convergence rate and enhancing the global exploration ability of the algorithm.Meanwhile,the mutative scale chaos mutation operator based on the piecewise linear chaotic map（PWLCM） mapping function is constructed for boosting the ability of escaping local optimum.Simulation results on benchmark functions show that the proposed algorithm can avoid local optimum effectively,and achieves significant improvements in terms of convergence speed and accuracy.