Landscape-assisted multi-operator differential evolution for solving constrained optimization problems. (30th December 2020)

• Record Type: Journal Article
• Authors: Sallam, Karam M.; Elsayed, Saber M.; Sarker, Ruhul A.; Essam, Daryl L.
• Is Part Of: Expert systems with applications. Volume 162(2020)
• Publication Date: 2020-12-30
• Subjects: Evolutionary algorithms -- Differential evolution -- Landscape analysis -- Adaptive operator selection -- Constrained optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113033
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A multi-operator Differential Evolution Algorithm is proposed. A landscape-based adaptive operator selection mechanism is developed. Three Constrained benchmark optimization problems had been solved. Ten real-world constrained optimization problems had been solved. Experiments showed that the proposed method outperforms state-of-the-art algorithms. Abstract: Over time, many differential evolution (DE) algorithms have been proposed for solving constrained optimization problems (COPs). However, no single DE algorithm was found to be the best for many types of COPs. Although researchers tried to mitigate this shortcoming by using multiple DE algorithms under a single algorithm structure, while putting more emphasis on the best-performing one, the use of landscape information in such designs has not been fully explored yet. Therefore, in this research, a multi-operator DE algorithm is developed, which uses a landscape-based indicator to choose the best-performing DE operator throughout the evolutionary process. The performance of the proposed algorithm was tested by solving a set of constrained optimization problems, 22 from CEC2006, 36 test problems from CEC2010 (18 with 10D and 18 with 30D), 10 real-application constrained problems from CEC2011 and 84 test problems from CEC2017 (28 with 10D, 28 with 30D and 28 with 50D). Several experiments were designed and carried out, to analyze the effects of different components on the proposed algorithm's performance, and the results from the final variant of the proposed algorithm were compared with different variants of the same algorithm with different selection criteria. Subsequently, the best variant found after analyzing the algorithm's components, was compared to several state-of-the-art algorithms, with the results showing the capability of the proposed algorithm to attain high-quality results.