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Title: A hybrid self-adaptive sine cosine algorithm with opposition based learning
Authors: Gupta S.
Deep K.
Published in: Expert Systems with Applications
Abstract: Real-world optimization problems demand an efficient meta-heuristic algorithm which maintains the diversity of solutions and properly exploits the search space of the problem to find the global optimal solution. Sine Cosine Algorithm (SCA) is a recently developed population-based meta-heuristic algorithm for solving global optimization problems. SCA uses the characteristics of sine and cosine trigonometric functions to update the solutions. But, like other population-based optimization algorithms, SCA also suffers the problem of low diversity, stagnation in local optima and skipping of true solutions. Therefore, in the present work, an attempt has been made towards the eradication of these issues, by proposing a modified version of SCA. The proposed algorithm is named as modified Sine Cosine Algorithm (m-SCA). In m-SCA, the opposite population is generated using opposite numbers based on perturbation rate to jump out from the local optima. Secondly, in the search equations of SCA self-adaptive component is added to exploit all the promising search regions which are pre-visited. To evaluate the effectiveness in solving the global optimization problems, m-SCA has been tested on two sets of benchmark problems – classical set of 23 well-known benchmark problems and standard IEEE CEC 2014 benchmark test problems. In the paper, the performance of proposed algorithm m-SCA is also tested on five engineering optimization problems. The conducted statistical, convergence and average distance analysis demonstrate the efficacy of the proposed algorithm to determine the efficient solution of real-life global optimization problems. © 2018 Elsevier Ltd
Citation: Expert Systems with Applications (2019), 119(): 210-230
Issue Date: 2019
Publisher: Elsevier Ltd
Keywords: Benchmark test problems
Engineering application problems
Opposition based learning
Population based algorithms
Sine Cosine algorithm (SCA)
ISSN: 9574174
Author Scopus IDs: 57209786185
Author Affiliations: Gupta, S., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Funding Details: The first author gratefully acknowledges the Ministry of Human Resource and Development Ministry of Human Resource and Development (MHRD), Govt. of India , India for their financial support. Grant no. MHR-02-41-113-429 .
Corresponding Author: Gupta, S.; Department of Mathematics, Indian Institute of Technology RoorkeeIndia; email:
Appears in Collections:Journal Publications [MA]

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