A new evolutionary algorithm for non-linear economic dispatch

• Published in: Expert systems with applications 2011-09, Vol.38 (10), p.13301-13309
• Main Authors: Niknam, Taher, Firouzi, Bahman Bahmani, Mojarrad, Hasan Doagou
• Format: Article
• Language: English
• Subjects: Chaotic Modified Shuffled Frog Leaping Algorithm; Economic dispatch; Nonlinear optimization
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2011.04.151

► This paper presents a modified shuffled frog Leaping algorithm for the ED problem. In this paper a new method is proposed to modify the worst frog’s position. This proposed approach is called Modified Shuffle Frog Leaping Algorithm (MSFLA). Also, in order to improve the algorithm’s stability and the ability to search the global optimum, a Chaotic Local Search (CLS) is used to get rid of the local optima. Reduce fossil fuel resources; increasing established new power generation unit costs; and ever growing demand for electric energy necessitate optimal economic dispatch (ED) in today’s electric power systems. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. One of the recently proposed evolutionary algorithms is the Shuffled Frog Leaping Algorithm (SFLA). In the original SFLA, every frog updates its position according to the best solution, because of the influence of the local best solution, every frog will constringe about the local best solution quickly. In this paper a new method is proposed to modify the worst frog’s position. This proposed approach is called Modified Shuffle Frog Leaping Algorithm (MSFLA). Also, in order to improve the algorithm’s stability and the ability to search the global optimum, a Chaotic Local Search (CLS) is used to get rid of the local optima. The proposed algorithm, called Chaotic Modified Shuffled Frog Leaping Algorithm (CMSFLA), is used to solve the ED problem considering the valve-point loading effects, multi-fuel and prohibited operating zones. The proposed algorithm is tested on different sample systems and its results are compared with other methods.