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A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization
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DOI：10.1007/s40747-023-01269-z.png)
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Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-parameter optimization problems, demonstrates its outstanding local search capability. In this study, two mechanisms are proposed and integrated into particle swarm optimization for single-objective numerical optimization. A novel ratio adaptation scheme is utilized for calculating the proportion of subpopulations and intermittently invoking the sequential quadratic programming for local search start from the best particle to seek a better solution. The novel particle swarm optimization variant was validated on CEC2013, CEC2014, and CEC2017 benchmark functions. The experimental results demonstrate impressive performance compared with the state-of-the-art particle swarm optimization-based algorithms. Furthermore, the results also illustrate the effectiveness of the two mechanisms when cooperating to achieve significant improvement.
Keyword:
Particle swarm optimization
Ratio adaptation scheme
Sequential quadratic programming
Single-objective numerical optimization
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4.6
论文数:
2.1K
被引数:
6.6K
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