University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 7 1 2016 05 21 Real-Coded Genetic Algorithm with Smart Mutation for Solving Nonconvex Economic Dispatch Problems Real-Coded Genetic Algorithm with Smart Mutation for Solving Nonconvex Economic Dispatch Problems 13 22 20712 10.22108/isee.2016.20712 FA Naser Ghorbani University of Tabriz Ebrahim Babaei University of Tabriz Journal Article 2016 11 23 In this paper, real-coded genetic algorithm with smart mutation (RCGA-SM) is proposed to solve the economic dispatch (ED) problem. In the proposed method, the required controlling process is accomplished on the total amount of chromosomes and consequently there is no need to use penalty cost function for controlling sum of variables in solving economic dispatch problem. This method will begin to explore the optimal answer just within the logic and acceptable zone in addition to its capability in reducing the search range. In order to show the performance and the efficiency of the proposed method, the ED problem considering several constraints is solved in 6, 15, and 40 units systems through the proposed technique. The proposed coding could effectively escapes from infeasible solutions. Thereby search efficiency and solution quality are dramatically improved. The obtained results are compared with other advanced technical algorithms, which well depicts the superiority of the RCGA-SM technique over the others compared methods. In this paper, real-coded genetic algorithm with smart mutation (RCGA-SM) is proposed to solve the economic dispatch (ED) problem. In the proposed method, the required controlling process is accomplished on the total amount of chromosomes and consequently there is no need to use penalty cost function for controlling sum of variables in solving economic dispatch problem. This method will begin to explore the optimal answer just within the logic and acceptable zone in addition to its capability in reducing the search range. In order to show the performance and the efficiency of the proposed method, the ED problem considering several constraints is solved in 6, 15, and 40 units systems through the proposed technique. The proposed coding could effectively escapes from infeasible solutions. Thereby search efficiency and solution quality are dramatically improved. The obtained results are compared with other advanced technical algorithms, which well depicts the superiority of the RCGA-SM technique over the others compared methods. https://isee.ui.ac.ir/article_20712_f9aa9547faa844ce6b62061122619350.pdf