The Chaotic Social Group Optimization for the Economic Dispatch Problem.

Economic dispatch problem is found in the recent focus of specialists, solving this problem being based especially on metaheuristic algorithms. Social Group Optimization (SGO) is a new metaheuristic algorithm that models human behavior to solve a certain problem, and it has been successfully applied to optimize mathematical functions. This paper investigates the behavior of the SGO algorithm equipped with five chaotic maps (Logistic, Double, Iterative, Singer and Cat), applied to solve the economic dispatch problem with multiple fuel sources. The new algorithm, called Chaotic Social Group Optimization (CSGO), results from including the sequences generated by chaotic maps in the heuristics specific to the SGO algorithm. The performance of the CSGO chaotic algorithm is tested on four systems having various dimensions (10-units, 20-units, 30-units, and 40-units) and characteristics. The SGO and CSGO algorithms are compared with each other and with other algorithms. The obtained results indicate that CSGO algorithms equipped with any of the chaotic maps mentioned has a higher average performance than SGO algorithm. The application of CSGO, compared to SGO, leads to a reduction of the fuel cost between (0.6-6.7) $/h for the analyzed systems. [ABSTRACT FROM AUTHOR]