This paper introduces a Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA), designed to enhance the performance of the standard Black Widow Algorithm (BW) in solving complex optimization problems. The proposed algorithm integrates four key strategies: initializing the population using Tent chaotic mapping to enhance diversity and initial exploratory capability; implementing mutation optimization on the least fit individuals to maintain dynamic population and prevent premature convergence; incorporating a non-linear inertia weight to balance global exploration and local exploitation; and adding a random perturbation strategy to enhance the algorithm's ability to escape local optima. Evaluated through a series of standard test functions, the MSBWOA demonstrates significant performance improvements in various dimensions, particularly in convergence speed and solution quality. Experimental results show that compared to the traditional BW algorithm and other existing optimization methods, the MSBWOA exhibits better stability and efficiency in handling a variety of optimization problems. These findings validate the effectiveness of the proposed strategies and offer a new solution approach for complex optimization challenges.

翻译：本文提出了一种多策略改进型黑寡妇优化算法（MSBWOA），旨在增强标准黑寡妇算法（BW）在求解复杂优化问题时的性能。所提算法融合了四种关键策略：利用帐篷混沌映射初始化种群以提升多样性与初始探索能力；对适应度最差的个体实施变异优化以维持种群动态性并防止早熟收敛；引入非线性惯性权重以平衡全局探索与局部开发；以及添加随机扰动策略以增强算法逃离局部最优的能力。通过一系列标准测试函数进行评估，MSBWOA在多个维度上展现出显著的性能提升，特别是在收敛速度和解质量方面。实验结果表明，与传统的BW算法及其他现有优化方法相比，MSBWOA在处理各类优化问题时具有更好的稳定性和效率。这些发现验证了所提策略的有效性，并为复杂优化挑战提供了一种新的求解方案。