求解工程优化问题的混合白鲸优化算法

A hybrid beluga optimization algorithm for solving engineering optimization problems

针对传统方法在解决带有复杂约束的工程优化问题时的不足,提出了一种基于交叉变异的混合白鲸优化算法(hybrid crossover variation beluga whale optimization,HCVBWO)。首先采用佳点集映射初始化种群从而增加种群的多样性;其次采用交叉变异策略增强了算法中期的开发能力;最后采用自适应混合扰动策略平衡了算法后期的局部和全局搜索能力。将HCVBWO算法与其他6种算法在IEEE CEC2014进行仿真试验,结果证明了HCVBWO具有良好的寻优能力和鲁棒性,此外,将HCVBWO算法运用到2种机械工程设计问题以及1个生产调度问题中,验证了所提算法在工程优化问题中的优越性。

A hybrid crossover variation beluga whale optimization(HCVBWO)is proposed to address the limitations of traditional methods in solving engineering optimization problems with complex constraints.Firstly,the algorithm utilizes an optimal point set mapping to initialize the population,thereby increasing the diversity of the population.Secondly,a cross-variation strategy is employed to enhance the algorithm’s mid-term development capability.Finally,an adaptive mixed perturbation strategy is used to balance the algorithm’s late-stage local and global search capabilities.The HCVBWO algorithm is compared with six other algorithms using simulations on the IEEE CEC2014 benchmark test set,and the results demonstrate the algorithm’s strong optimization capability and robustness.Furthermore,the application of the HCVBWO algorithm to two mechanical engineering design problems and a production scheduling problem verifies its superiority in engineering optimization.