A modified crow search algorithm (MCSA) for solving economic load dispatch problem

• Published in: Applied soft computing Vol. 71; pp. 51 - 65
• Main Authors: Mohammadi, Farid, Abdi, Hamdi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2018
• Subjects: Economic load dispatch (ELD); Evolutionary algorithms (EAs); Modified crow search algorithm (MCSA); Optimization; Modified crow search algorithm (MCSA); Evolutionary algorithms (EAs); Economic load dispatch (ELD); Optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.06.040

•Applying a novel evolutionary optimization algorithm namely MCSA to ELD problem.•Proposing two modification methods for improving the CSA performance.•Applying the MCSA to five well-known ELD test systems.•Employing four well-known benchmark functions to verify the MCSA.•Addressing the MCSA as a highly competitive with some previous algorithms. This paper presents a novel evolutionary optimization algorithm namely the modified crow search algorithm (MCSA) for solving the non-convex economic load dispatch (ELD) problem which improves the crow search algorithm (CSA) by an innovative selection of the crows and adaptive adjustment of the flight length. MCSA is a population-based technique based on the intelligent behavior of the crows in finding food sources. In MCSA, each crow saves its food in hiding-places for the time it needs. Also, each crow searches environment to find the better foods by stealthily following other crows to discover their hiding-places. The proposed MCSA develops the search capability of crows in the original CSA and introduces a new way by which a destination is selected by a crow to follow. To indicate the applicability of MCSA in the ELD problem, it is applied on five different well-known test systems. The results are compared in terms of the solution quality, robustness, and computing time with other methods implying that the proposed method has a superior performance than the other techniques.