| 摘 要: 针对鲸鱼优化算法(WOA)收敛速度慢、收敛精度低、搜索能力不足的缺点，提出了一种改进的多领导鲸鱼优化算法(IWOA)。该算法引入了多领导机制，有利于提高种群的多样性，防止陷入局部最优。引入莱维飞行机制，将最优个体进行随机扰动，加快收敛速度，防止陷入局部最优。通过CEC2014中的六个标准函数进行测试，给出了运行30次的平均值和标准方差，并与当前最先进的其他算法进行比较。收敛图表明了该算法收敛速度快、收敛精度高；箱线图表明了该算法的稳定性。最后用该算法解决三个经典的工程优化问题，该算法相较于其他算法均取得了最小值，表明了该算法具有优秀的搜索能力与开发能力。 |
| 关键词: 鲸鱼优化算法；多领导；莱维飞行；标准函数；工程优化问题 |
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中图分类号: TP312
文献标识码: A
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| An Improved Multi-leader Whale Optimization Algorithm |
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YU Xianxing
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(School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China )
yvxianxing@163.com
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| Abstract: Aiming at the shortcomings of whale optimization algorithm (WOA), such as slow convergence speed, low convergence accuracy and insufficient search ability, this paper proposes an improved multi-leader whale optimization algorithm (IWOA). The algorithm introduces a multi-leader mechanism, which is conducive to improving the diversity of the population and preventing it from falling into the local optimum. The Levy flight mechanism is introduced to randomly perturb the optimal individual to accelerate the convergence speed and prevent it from falling into the local optimum. The algorithm is tested by six benchmark functions of CEC2014, and the average value and standard deviation of 30 runs are given. Compared with other state-of-the-art algorithms, the convergence graphs show the fast convergence speed and high convergence accuracy of the proposed algorithm, and the boxplots show its stability. Finally, the algorithm is used to solve three classical engineering optimization problems. Compared with other algorithms, the proposed algorithm achieves the minimum value, which shows its excellent search and development ability. |
| Keywords: whale optimization algorithm; multi-leader; Levy flight; standard function; engineering optimization problem |
