Orthogonally Initiated Particle Swarm Optimization with Advanced Mutation for Real-Parameter Optimization
Indu Bala, Dikshit Chauhan, Lewis Mitchell · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2024
This article introduces an enhanced particle swarm optimizer (PSO), termed Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal array-based learning approach to cultivate an improved initial swarm for PSO, significantly boosting the adaptability of swarm-based optimization algorithms. The article further presents archive-based learning strategies, dividing the population into regular and elite sub-groups. Each subgroup employs distinct learning mechanisms. The regular group utilizes efficient learning schemes derived from three unique archives, which categorize individuals based on their quality levels. Additionally, a mutation strategy is implemented to update the positions of elite individuals. Comparative studies are conducted to assess the effectiveness of these learning strategies in OPSO-m, evaluating its optimization capacity through exploration-exploitation dynamics and population diversity analysis. The proposed OPSO-m model is tested on real-parameter challenges from the CEC 2017 suite in various dimensional search spaces. OPSO-m exhibits distinguished performance in the precision of solutions, rapidity of convergence, efficiency in search, and robust stability, thus highlighting its superior aptitude for resolving optimization problems.