Particle swarm optimization with state-based adaptive velocity limit strategy

TL;DR

This paper introduces PSO-SAVL, a particle swarm optimization variant that adaptively adjusts velocity limits based on the particles' evolutionary state, improving optimization performance especially in high-dimensional problems.

Contribution

The paper proposes a novel state-based adaptive velocity limit strategy for PSO, enhancing search efficiency and avoiding local optima compared to existing methods.

Findings

PSO-SAVL outperforms traditional PSO on benchmark functions.

The method scales well to high-dimensional and large-scale problems.

Hyper-parameter sensitivity analysis provides guidance for practical implementation.

Abstract

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.