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DNA sequence-driven multi-strategy particle swarm optimization for global optimization
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DOI：10.1007/s10586-025-05280-6.png)
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Abstract
En 中文
This paper proposes an improved multi-strategy particle swarm optimization algorithm with DNA sequence selection state (DSMPSO), aimed at addressing the issues of weak diversity and premature convergence in traditional particle swarm optimization (PSO). First, DSMPSO introduces a DNA-SDM search framework based on the DNA sequence similarity of particle dimensions, enabling the selection of suitable search paradigms through evolutionary state determination. Second, a neighborhood-based differential elite subpopulation optimization mechanism is incorporated to further enhance particle diversity and accelerate convergence. Finally, a modified skew-tent chaotic acceleration coefficient is employed to balance the search modes. In the CEC2022 benchmark test for complex problems, DSMPSO outperforms six state-of-the-art PSO variants and standard PSO, demonstrating exceptional performance when compared with three top algorithms. Moreover, DSMPSO showcases strong practicality in solving economic dispatch problem of microgrids and three real-world engineering problems.
Keywords:
Particle swarm optimization
State determination
Dominant subpopulation
Acceleration coefficient
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