摘要： 针对正弦余弦算法（Sine - Cosine Algorithm, SCA）在优化问题中存在的收敛速度慢、策略单一的问题，以及在局部动态路径规划中存在的路径曲折及动态避障震荡的不足，本文提出一种多策略自适应网络正弦余弦算法（Multi - Strategy Adaptive Network Sine - Cosine Algorithm, MANSCA）。首先，通过引入差分策略、全局更新策略和局部更新策略，构建多策略自适应网络，并基于轮盘赌选择机制动态调整策略权重，增强算法的全局搜索与局部开发能力。其次，针对路径规划问题，提出目的导向策略，通过改进引力势场函数平衡目标点吸引力，减少路径震荡；同时设计动态避障策略，结合障碍物运动方向调整机器人避障方向，避免传统节点删除法的方向性缺失问题。首先，MANSCA算法的有效性在CEC2015基准函数和CEC2022基准函数问题中得到了验证，并且与其他最新元启发算法相比具有竞争力。其次，MANSCA应用于包含静态和动态障碍的复杂环境中的多机器人局部路径规划,所提出的算法相较于SCA在总行驶距离和最大节点数上分别降低了约62.6%和63%。

Abstract: Aiming at the problems of slow convergence and single strategy of the Sine - Cosine Algorithm (SCA) in optimization problems, as well as the issues of zig - zag paths and oscillation in dynamic obstacle avoidance in local dynamic path planning, this paper proposes a Multi - Strategy Adaptive Network Sine - Cosine Algorithm (MANSCA). Firstly, by introducing differential strategies, global update strategies and local update strategies, a multi - strategy adaptive network is constructed, and the strategy weights are dynamically adjusted based on the roulette wheel selection mechanism to enhance the global search and local development abilities of the algorithm. Secondly, for path planning problems, a goal - oriented strategy is proposed. The gravitational potential field function is improved to balance the attraction of the target point and reduce path oscillation. At the same time, a dynamic obstacle avoidance strategy is designed. The obstacle - avoidance direction of the robot is adjusted by combining it with the moving direction of obstacles, which avoids the problem of direction - missing in the traditional node - deletion method.The effectiveness of the MANSCA algorithm has been verified in the CEC2015 benchmark functions and CEC2022 benchmark function problems, and it is competitive compared with other latest meta - heuristics. When applied to multi - robot local path planning in complex environments with static and dynamic obstacles, the proposed algorithm respectively reduces the total travel distance and maximum node number by about 62.6% and 63% compared with SCA.