Particle swarm optimization (PSO) is widely applied to various practical problems due to its strong optimization capability and flexibility. However, when tackling complex optimization tasks, it suffers from shortcomings such as premature convergence and an imbalance between global exploration and local exploitation. To address these issues, this study proposes a multi-strategy cooperative particle swarm optimization algorithm (MSCPSO). MSCPSO divides the population into leaders and followers based on fitness and integrates diverse learning strategies to enhance performance. First, a nonlinear adaptive inertia weight is proposed to dynamically adjust inertia according to particle roles, effectively balancing exploration and exploitation. Second, a weighted learning strategy is introduced, which assigns weights based on leader fitness values to guide particles more efficiently toward promising solution regions. Third, a fitness-distance balance mechanism is designed to maintain population diversity in the early stage, accelerate convergence in the later stage, and reduce the probability of falling into local optima. Finally, in the later iterations of the algorithm, a terminal replacement mechanism is designed to replace the worst global particle, reducing population diversity to accelerate convergence. Comparative experiments on CEC2014, CEC2017, and CEC2022 test suites against seven heuristic algorithms, eleven PSO variants, and eight state-of-the-art algorithms show that multi-strategy cooperation significantly enhances PSO performance. MSCPSO outperforms most compared algorithms. Finally, MSCPSO is applied to 3D UAV path planning in complex environments. Across 12 scenarios of varying complexity, MSCPSO demonstrates the ability to generate more feasible and efficient paths in most cases.
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