求解复杂约束优化问题的多策略混合麻雀搜索算法

针对麻雀搜索算法面对具有强约束、非凸性和不可微特征的复杂问题所存在的开发与探索能力不平衡、易陷入局部最优、过早收敛和种群多样性较低等不足,提出一种求解复杂约束优化问题的多策略混合麻雀搜索算法.首先,利用反向学习策略构建双向初始化机制,以达到获得分布更优的初始种群的目的;其次,设计一种基于交叉与变异算子的位置更新公式,扩大搜索范围,丰富搜索机制,以平衡算法探索和开发能力,同时提高算法的收敛精度和速度;最后,使用社区学习策略对种群进行精炼,强化开发能力与跳出局部极值的能力,并保持种群的多样性.分别在CEC2017的28个实数约束优化问题和1个工程优化问题上进行了性能评估,实验结果表明,所提出的算法对比其他优化算法具有寻优能力强、收敛精度高、收敛速度快等优势,可有效解决复杂约束优化问题.

Multi-strategy hybrid sparrow search algorithm for complex cons-trained optimization problems

In view of the shortcomings of the sparrow search algorithm in the face of complex problems with strong constraints, non-convexity and non-differentiability, such as unbalanced exploitation and exploration ability, easy to fall into local optimum, premature convergence and low population diversity, a multi-strategy hybrid sparrow search algorithm for complex constrained optimization problems is proposed. Firstly, the opposition-based learning strategy is used to construct a bi-directional initialization mechanism to achieve the purpose of obtaining the initial population with better distribution. Then, a position update formula based on the crossover and mutation operator is designed to expand the search range and enrich the search mechanism for balancing the exploration and exploitation ability of the algorithm, while improving the convergence accuracy and speed of the algorithm. Finally, the community learning strategy is used to refine the population, strengthen the exploitation ability and the ability to jump out of the local optima, and maintain the diversity of the population. The performance of the proposed algorithm is evaluated on 28 real constrained optimization problems of CEC2017 and 1 engineering optimization problems. The experimental results show that the proposed algorithm compared with other optimization algorithms has advantages such as stronger optimization ability, higher convergence accuracy, faster convergence speed and so on, which can be used to effectively solve complex constrained optimization problems.