An Improved Whale Optimization Algorithm Based on Nonlinear Function and Local Search

Jie Liu
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Abstract

In order to improve the searching ability of whale optimization algorithm in continuous optimization function, an improved whale optimization algorithm based on nonlinear function and local search (NLWOA) is proposed. First, because the linear decreasing convergence function cannot balance the exploitation and exploration ability of WOA, this paper designs a nonlinear convergence function to make the algorithm have outstanding exploitation ability in the early stage and excellent exploration ability in the later stage. Second, the original whale optimization algorithm is too divergent in the random search stage. Thus, this paper introduces the historical optimization of whale population and individual. Finally, the proposed algorithm is tested in 23 benchmark functions and compared with other optimization algorithms. The experimental results show that NLWOA can better balance the exploitation and exploration capabilities. So NLWOA has better optimization capability.
基于非线性函数和局部搜索的改进鲸鱼优化算法
为了提高鲸鱼优化算法在连续优化函数中的搜索能力,提出了一种基于非线性函数和局部搜索的改进鲸鱼优化算法(NLWOA)。首先,由于线性递减收敛函数无法平衡WOA的开发和探索能力,本文设计了非线性收敛函数,使算法在早期具有突出的开发能力,在后期具有优异的探索能力。其次,原有的鲸鱼优化算法在随机搜索阶段过于发散。因此,本文介绍了鲸鱼种群和个体的历史优化。最后,在23个基准函数中对该算法进行了测试,并与其他优化算法进行了比较。实验结果表明,NLWOA能较好地平衡开采和勘探能力。因此,NLWOA具有较好的优化能力。
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