A hybrid self-adaptive sine cosine algorithm with opposition based learning

•A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based learning.•Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.•The m-SCA is also tested on engineering optimization...

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Bibliographic Details
Published in:Expert systems with applications Vol. 119; pp. 210 - 230
Main Authors: Gupta, Shubham, Deep, Kusum
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.04.2019
Elsevier BV
Subjects:
Adaptive algorithms
Algorithms
Benchmark test problems
Benchmarks
Engineering application problems
Expert systems
Global optimization
Heuristic
Heuristic methods
Machine learning
Opposition based learning
Optimization
Population (statistical)
Population based algorithms
Searching
Self-adaptation
Sine Cosine algorithm (SCA)
Stagnation
Trigonometric functions
Engineering application problems
Self-adaptation
Sine Cosine algorithm (SCA)
Benchmark test problems
Population based algorithms
Opposition based learning
ISSN:0957-4174, 1873-6793
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Summary:•A new method to solve global optimization and engineering problems called m-SCA.•The m-SCA improves the SCA using self-adaptation and opposition based learning.•Two set of benchmarks (classical and CEC 2014) is taken to evaluate the performance.•The m-SCA is also tested on engineering optimization problems.•Comparisons illustrate the improvement on the performance of m-SCA. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.10.050