Enhanced Particle Swarm Optimization through Multi-Mechanism Integration: Application to Power System Economic Dispatch

Abstract

In this paper, a Hybrid Adaptive PSO method (HAPSO) is developed to solve the economic load dispatch problem over a standard IEEE 30-bus system. The proposed HAPSO combines chaotic initialization, adaptive inertia weight adjustment, time-varying acceleration coefficients, Lévy flight-based exploration, opposition-based learning, and Nelder–Mead simplex optimization into a single PSO paradigm. This set of PSO mechanisms enhances the randomification of the population and the global-local searches support. The proposed approach was implemented with transmission losses considered via B-loss coefficients and a quadratic fuel cost function. What's more, for performance and statistical significance, we conducted more than thousand optimization runs for the seven algorithm variants (curves), consisting of 30 runs each run being an independent optimization. The superior HAPSO variant achieved the best-known minimum fuel cost of $774.47/hour refining the benchmark value of $786.03/hour acquired based on the identical modelling.” By consistently surpassing the reference solution on other variants, it illustrates the systematic nature of the optimization. All simulations were performed with full generators operational constraints. The results obtained reveal that the proposed HAPSO technique is an efficient.