混合策略改进鲸鱼优化算法

针对标准鲸鱼优化算法（whale optimization algorithm，WOA）易陷入局部最优解、收敛精度低、收敛速度慢等问题，提出一种利用混合策略改进的鲸鱼优化算法（multi-strategy improved whale optimization algorithm，MSIWOA）。首先采取精英反向策略初始化种群，提高初始种群质量；其次，采取卡方分布的逆累积分布函数更新收敛因子以实现全局探索和局部开发的平衡；然后利用改进氏族拓扑结构强化种群的全局探索能力，并提高算法收敛速度；最后采取Circle映射产生混沌解，结合贪婪策略保留较优解，以帮助种群跳出局部最优解。通过对10个基准测试函数以及CEC2019测试函数进行仿真实验，结果表明，MSIWOA在收敛精度和收敛速度上均有较明显的提升。

Multi-strategy improved whale optimization algorithm

Aiming at the problems of standard whale optimization algorithm(WOA) with low accuracy, slow convergence and easy to fall into local best solutions, this paper proposed a multi-strategy improved whale optimization algorithm(MSIWOA). Firstly, MSIWOA adopted the elite reverse strategy to initialize the population to improve the quality of the initial population. Secondly, it used the inverse cumulative distribution function of chi-square distribution to update the convergence factor to achieve the balance between global exploration and local development. Then, it used the improved clan topology to strengthen the global exploration ability of the population and improve the convergence speed of the algorithm. Finally, it generated the chaotic solution by Circle mapping, and retained the optimal solution by greedy strategy to help the population jump out of the local optimal solution. The simulation results of 10 benchmark test functions and CEC2019 test function show that the convergence accuracy and convergence speed of MSIWOA are significantly improved.