基于黄金莱维引导机制的阿基米德优化算法

Archimedes optimization algorithm based on golden Levy guidance mechanism

针对标准阿基米德优化算法(AOA)在求解优化问题时存在全局探索能力弱、收敛速度慢和求解精度低等问题,提出一种多策略阿基米德优化算法(MSAOA)。首先,利用变区间初始化策略,使得初始种群尽可能地靠近全局最优解,从而提高初始解的质量;其次,提出黄金莱维引导机制,以提高算法在迭代后期的种群多样性;最后,在维持种群多样性的前提下,引入自适应波长算子,以达到提高算法搜索效率的目的。将所提算法与均衡器算法(EO)、正余弦算法(SCA)以及灰狼优化算法(GWO)在20个基准测试函数上进行比较实验。实验结果表明,所提算法具有更高的寻优精度和收敛速度,并将所提算法应用于4个机械设计实例中,再次验证了所提算法的有效性和优越性。

Aiming at the problems of standard Archimedes Optimization Algorithm(AOA)in solving optimization problems,such as weak global exploration ability,slow convergence and low solution accuracy,a Multi-Strategy improved AOA(MSAOA)was proposed. Firstly,the variable interval initialization strategy was used to make initial population near to the global optimal solution as close as possible to improve the quality of initial solution. Secondly,the golden Levy guidance mechanism was proposed to improve the population diversity of the algorithm in later iteration stage. Thirdly,the adaptive wavelength operator was introduced to achieve the purpose of improving search efficiency of the algorithm while maintaining diversity of population. The proposed algorithm was compared with Equilibrium Optimizer(EO),Sine Cosine Algorithm(SCA)and Grey Wolf Optimizer(GWO)on 20 benchmark test functions. Experimental results show that the proposed algorithm has higher optimization accuracy and convergence speed. And the proposed algorithm was applied to four mechanical design examples to verify the effectiveness and superiority of the proposed algorithm again.