Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

TLDR

Power systems frequently face nonlinear optimization challenges, where traditional analytical methods can converge slowly and struggle with high dimensionality, making swarm‑based heuristics such as particle swarm optimization (PSO) a promising alternative for large‑scale problems. This paper offers a comprehensive review of PSO fundamentals and variants, alongside a survey of power‑system applications that have leveraged the algorithm’s strengths. The authors detail application‑specific PSO configurations—including algorithm type, particle representation, and optimal fitness functions—to guide practitioners in deploying the method.

Abstract

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.