Abstract
In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.
Highlights
In order to balance the exploration and exploitation capabilities of the Particle Swarm Optimization (PSO) algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO)
The proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position, and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. e cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed
Based on the multilearning strategy, the global best position of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. is strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. en, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. e test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants
Summary
This section discusses the mechanism of the basic PSO algorithm. en, the PSO variants are reviewed according to the different improvement ideas of scholars. E adaptive control of the parameters is one of the widely used strategies to enhance the searching performance of the basic PSO algorithm. Tang et al proposed a feedback learning PSO with quadratic inertia weight (FLPSO-QIW), and the parameters are controlled by introducing a fitness feedback mechanism [25]. Overall, adjusting parameters can improve the performance of the PSO algorithm, but this strategy is mainly temporary [28]. Another category of PSO variants introduces new learning strategies to enhance population diversity. E PSO algorithm is a stochastic global optimization technique, and the searching performance is closely related to the probability distribution of the population. Zhao and Suganthan proposed two local bests based multiobjective PSO which focuses the search around small regions in the parameter space in the vicinity of the best existing fronts and applies different mutation operators to different subswarms to accelerate the convergence [49]
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