Keywords
Abstract
This paper introduces a novel hybrid optimization algorithm that integrates Particle Swarm Optimization (PSO) and Simulated Annealing (SA), thereby simulating the human “exploration - convergence” cognitive paradigm. The algorithm combines the collective exploration capacity of PSO with the stochastic convergence mechanism of SA. It models the cognitive process where humans broadly explore the problem space and then concentrate on solution optimization during problem - solving. In terms of convergence speed and global optimization ability, it outperforms conventional PSO and SA algorithms. Additionally, it demonstrates superior performance in escaping local optima. This provides a fresh perspective for designing intelligent optimization algorithms based on cognitive simulation. The algorithm’s structure and mechanism are analyzed in detail. It first employs PSO for extensive exploration of the problem space. Subsequently, SA is utilized to fine - tune and optimize the solution. The algorithm’s parameters are carefully calibrated to ensure optimal performance. Extensive experimental results validate its effectiveness and superiority over traditional algorithms.
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