A Competitive Swarm Optimizer for Large Scale Optimization

TLDR

The algorithm is fundamentally inspired by particle swarm optimization yet conceptually distinct. The paper proposes a novel competitive swarm optimizer (CSO) for large‑scale optimization. The CSO updates particles through pairwise competition, omitting personal and global bests, and its convergence is theoretically proven while empirical analysis demonstrates balanced exploration and exploitation. Empirical results show that the CSO outperforms five state‑of‑the‑art metaheuristics on widely used large‑scale problems, solving up to 5000 dimensions, while theoretical analysis confirms convergence and balanced exploration/exploitation.

Abstract

In this paper, a novel competitive swarm optimizer (CSO) for large scale optimization is proposed. The algorithm is fundamentally inspired by the particle swarm optimization but is conceptually very different. In the proposed CSO, neither the personal best position of each particle nor the global best position (or neighborhood best positions) is involved in updating the particles. Instead, a pairwise competition mechanism is introduced, where the particle that loses the competition will update its position by learning from the winner. To understand the search behavior of the proposed CSO, a theoretical proof of convergence is provided, together with empirical analysis of its exploration and exploitation abilities showing that the proposed CSO achieves a good balance between exploration and exploitation. Despite its algorithmic simplicity, our empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.