Gradient gravitational search: An efficient metaheuristic algorithm for global optimization. Issue 14 (17th March 2015)

• Record Type: Journal Article
• Authors: Dash, Tirtharaj; Sahu, Prabhat K.
• Is Part Of: Journal of computational chemistry. Volume 36:Issue 14(2015)
• Page Start: 1060
• Page End: 1068
• Publication Date: 2015-03-17
• Subjects: gradient gravitational search -- global optimization -- metaheuristic -- protein folding -- potential energy; Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.23891
• Languages: English
• ISSNs: 0192-8651

Abstract

Abstract : The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient‐based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two‐dimensional and three‐dimensional off‐lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc. Abstract : The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking center stage with regard to efficient algorithms, computational cost, and accuracy. The gradient‐based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum, has been reported. The GGS approach has been applied to computational chemistry problems for finding the minimal value potential energy of two‐dimensional and three‐dimensional off‐lattice protein models.