# Dual-Subpopulation Competitive Particle Swarm Optimization with Engineering Applications

## TL;DR

This paper introduces a new optimization algorithm that improves performance by splitting the population into two subpopulations focused on exploration and exploitation.

## Contribution

The novel dual-subpopulation competitive PSO (DCPSO) dynamically balances exploration and exploitation through adaptive migration of particles.

## Key findings

- DCPSO outperforms standard PSO and other algorithms on the CEC2017 benchmark suite.
- The algorithm shows robust performance on engineering design problems and hybrid functions.

## Abstract

Particle swarm optimization (PSO) is a widely used bio-inspired optimization algorithm, yet maintaining an effective balance between exploration and exploitation remains challenging. Most existing PSO variants rely on static or predefined regulation strategies, which restrict their adaptability to evolving search states and may lead to premature convergence or search stagnation. Inspired by division of labor and competitive selection mechanisms in biological populations, this paper proposes a dual-subpopulation competitive particle swarm optimization (DCPSO). In DCPSO, the population is explicitly partitioned into exploration and exploitation subpopulations with distinct search roles. A dynamic competition mechanism is designed to evaluate recent search progress, based on which stagnated particles are adaptively migrated between subpopulations, enabling flexible reallocation of computational resources during the optimization process. Experimental results on the CEC2017 benchmark suite demonstrate that DCPSO consistently outperforms standard PSO and several representative state-of-the-art algorithms, achieving statistically significant improvements on the majority of benchmark functions, particularly on hybrid and composition problems. Additional experiments on engineering design problems further verify the robustness, convergence stability, and practical effectiveness of DCPSO.