A New Hybrid Fuzzy Adaptive Particle Swarm Optimization for Non-convex Economic Dispatch

In the electric power systems, there is a wide range of problems involving optimization processes. Among them, Economic Dispatch (ED) is one of the most important problems in the operation and management. Recently, modern meta-heuristic algorithms have been considered as effective tools for nonlinear optimization problems with applications to power systems scheduling. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. The basic disadvantage of classic PSO is the fact that it may miss the optimum and provide a near-optimum solution in a limited runtime period. Moreover, the premature convergence of PSO degrades its performance and reduces its search capability that leads to a higher probability towards obtaining a local optimum. Therefore, this paper proposes a novel and efficient hybrid algorithm based on combing fuzzy adaptive PSO and Differential Evolution (DE), called FAPSO-DE, to solve ED problems. PSO is the main optimizer and the DE is used to maintain the population diversity and prevent leading to misleading local optima for every improvement in the solution of the PSO run. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors. In order to validate of the proposed algorithm, it is applied to a system consisting of 13 and 40 thermal units whose fuel cost function is calculated by taking account of the effect of valve-point loading.