EMOCSO: an efficient multi-objective competitive swarm optimizer for large-scale optimization

Abstract

Large-scale multi-objective optimization problems (LSMOPs) are crucial in real-world applications where balancing conflicting objectives is essential for decision-making. To overcome this limitation, we propose an efficient Multi-objective Competitive Swarm Optimizer (EMOCSO). The algorithm introduces three key innovations: (1) an Archive-driven Winner Learning Strategy that uses elite solutions to guide the search, (2) a Dual-layer Differential Neutral Update Mechanism to enhance diversity by adaptively updating “neutral” individuals (solutions with similar fitness), and (3) a Selective Spiral Archive Update Strategy to refine solutions through spiral-based local search. Comprehensive experiments on the LSMOP benchmark show that EMOCSO outperforms five state-of-the-art algorithms, demonstrating superior convergence and diversity in high-dimensional optimization scenarios. Moreover, in the application of active power allocation for regional photovoltaic clusters driven by meteorological data, EMOCSO effectively balances multiple objectives such as following dispatch instructions, stabilizing power output, and controlling uncertainty, providing operable solutions for actual power grid dispatching and verifying its engineering practical value.