Solution of an Economic Dispatch Problem Through Particle Swarm Optimization: A Detailed Survey – Part II

Abstract

Although particle swarm optimization (PSO) in its standard form performs extremely well for less complicated convex optimization problems involving reduced search space, it fails in finding global optimal solutions for more complicated nonconvex optimization problems with multiminima functions, thus exploring the promising search space less efficiently to ensure solution with superior quality. Guaranteeing the location of the global optimum through PSO becomes strenuous. The inherited premature convergence problem of PSO becomes more prominent while handling, especially the complex nonconvex problems. However, PSO has the ability to hybrid with other optimization techniques to ensure optimal global solution, better convergence characteristics, computational efficiency, and so on, while dealing with complex nonconvex problems. After presenting a detailed survey of the variants of PSO (involving variations in the basic structure of PSO) in part I, part II of this paper now comprehensively details all the hybrid forms (purely) of PSO applied to a constrained economic dispatch problem. How PSO overcomes its premature convergence problem while hybridizing with other optimization techniques is well-highlighted.

Keywords

• Convergence characteristics
• economic dispatch (ED) problem
• hybrid forms of particle swarm optimization (PSO)
• multiminima functions
• optimal global solution
• premature convergence problem