Communication and Self-Learning Strategies Incorporated State Transition Algorithm for Optimization of Complex Systems

The optimization of complex industrial systems is the key to improving the efficiency and safety of system operation. With the increasing scale of modern industry and the complexity of the production process, traditional optimization methods such as state transition algorithm (STA) are difficult to apply to the optimization problems of complex systems. Insufficient individual diversity of candidate solutions and the susceptibility of the algorithm to local optima are two main challenges that need to be addressed. This paper proposes a novel heuristic optimization method named CSL-STA, which integrates communication and self-learning strategies with STA. The communication strategy increases diversity during algorithm iteration, while the self-learning strategy increases the probability of escaping local optima. A series of comprehensive experiments have been conducted to verify the performance of the proposed method. These experimental results reveal that CSL-STA surpasses several existing optimization algorithms regarding global search capability and solution accuracy.