Water Evaporation Optimization: A novel physically inspired optimization algorithm. (15th April 2016)

• Record Type: Journal Article
• Authors: Kaveh, A.; Bakhshpoori, T.
• Is Part Of: Computers & structures. Volume 167(2016)
• Page Start: 69
• Page End: 85
• Publication Date: 2016-04-15
• Subjects: Metaheuristics -- Water Evaporation Optimization -- Molecular dynamics simulations -- Global optimization -- Global search -- Local search; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2016.01.008
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: A novel physically inspired non-gradient algorithm is developed for solution of global optimization problems. The algorithm is called Water Evaporation Optimization (WEO). WEO mimics the evaporation of a tiny amount of water molecules on the solid surface with different wettability. WEO is tested and analyzed in comparison to other existing methods on three sets of continuous test problems. 17 benchmark unconstrained functions, 13 classical benchmark constraint functions, and 3 benchmark engineering problems are studied. The results indicate that the proposed technique is highly competitive with other metaheuristics. Abstract: In this paper a novel physically inspired non-gradient algorithm is developed for solution of global optimization problems. The algorithm being called Water Evaporation Optimization (WEO) mimics the evaporation of a tiny amount of water molecules on the solid surface with different wettability which can be studied by molecular dynamics simulations. WEO is tested and analyzed in comparison to other existing methods on three sets of continuous test problems, a set of 17 benchmark unconstrained functions (consisting of three types of functions: unimodal, multimodal, and shifted and rotated functions), a set of 13 classical benchmark constraint functions, and three benchmark constraint engineering problems, reported in the specialized literature. The results obtained indicate that the proposed technique is highly competitive with other efficient well-known metaheuristics. The features used in WEO are analyzed and its potential implications for real size constrained engineering optimization problems are discussed in details.