A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization. (15th September 2020)

• Record Type: Journal Article
• Authors: Gupta, Shubham; Deep, Kusum; Mirjalili, Seyedali; Kim, Joong Hoon
• Is Part Of: Expert systems with applications. Volume 154(2020)
• Publication Date: 2020-09-15
• Subjects: Optimization -- Sine Cosine Algorithm -- Exploration and exploitation -- Multilayer perceptron -- Engineering optimization problems -- Algorithm -- Benchmark -- Grey Wolf Optimizer -- Particle Swarm Optimization -- Genetic Algorithm; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113395
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new method has been proposed, known as MSCA, for global optimization. The MSCA improves the SCA using a novel transition parameter and mutation operator. A set of 33 benchmark problems is used to examine the MSCA. The MSCA is also used to solve real-engineering problems and to train multilayer perceptron. Comparisons illustrate the improvement in the performance of the MSCA. Abstract: Inspired by the mathematical characteristics of sine and cosine trigonometric functions, the Sine Cosine Algorithm (SCA) has shown competitive performance among other meta-heuristic algorithms. However, despite its sufficient global search ability, its low exploitation ability and immature balance between exploitation and exploration remain weaknesses. In order to improve Sine Cosine Algorithm (SCA), this paper presents a modified version of the SCA called MSCA. Firstly, a non-linear transition rule is introduced instead of a linear transition to provide comparatively better transition from the exploration to exploitation. Secondly, the classical search equation of the SCA is modified by introducing the leading guidance based on the elite candidate solution. When the above proposed modified search mechanism fails to provide a better solution, in addition, a mutation operator is used to generate a new position to avoid the situation of getting trapped in locally optimal solutions during the search. Thus, the MSCA effectively maximizes the advantages of proposed strategies in maintaining a comparatively better balance of exploration and exploitation as compared to the classical SCA. The validity of the MSCA is tested on a set of 33 benchmark optimization problems and employed for training multilayer perceptrons. The numerical results and comparisons among several algorithms show the enhanced search efficiency of the MSCA.