Attention Learning Particle Swarm Optimization Algorithm Guided by Aggrega-tion Indicator

Abstract

Although the particle swarm optimization (PSO) algorithm has demonstrated good performance in solving many optimization problems, maintaining population diversity while ensuring convergence accuracy, preventing the swarm from getting trapped in local optima, and balancing exploration and exploitation remain important research problems for PSO algorithms. In response to these issues, this paper proposes an attention learning particle swarm optimization algorithm guided by aggregation indicator (ALPSO-AI). Firstly, to effectively maintain population diversity, the entire population is divided into equally sized subswarms, which are recombined during the evolution process. In each generation, different particles in the subswarms adaptively select multiple high-quality learning objects based on their performance. An external archive is established to guide the search and evaluate the evolutionary progress of the population. Secondly, an attention mechanism is introduced to assign different attention weights to each learning object based on the difference between its performance and the updated particle??s fitness value. Thus, a high-quality learning prototype is generated for particle updates. Different scales of attention allocation methods are designed to meet the diverse search requirements in the early and late stages of the search process, enabling both global and local search capabilities. Furthermore, an aggregation indicator is introduced to the archive, which assesses the current population??s evolutionary level by examining the similarity of fitness values around the current best particle. When the aggregation indicator reaches a threshold, local search is initiated to enhance the overall convergence capability of the algorithm. Experimental evaluations are conducted on the 28 benchmark functions from the CEC2013 test suite in both 30 and 50 dimensions. ALPSO-AI is compared with five popular PSO variants and other optimization algorithms. Experimental results confirm the superiority of ALPSO-AI. The effectiveness of attention learning and the aggregation indicator is also thoroughly validated.

Keywords

• particle swarm optimization (pso); attention mechanism; aggregation indicator; archive; local search