Improved sine cosine algorithm with crossover scheme for global optimization

Sine Cosine Algorithm is a recently developed algorithm based on the characteristics of sine and cosine trigonometric functions, to solve global optimization problems. This paper introduces a novel improved version of sine cosine algorithm, which enhances the exploitation ability of solutions and reduces the overflow of diversity present in the search equations of classical SCA. The proposed algorithm is named as [SCA, The key feature in the proposed algorithm is the hybridization of exploitation skills of crossover with personal best state of individual solutions and integration of self-learning and global search mechanisms. To evaluate these skills in ISCA, a classical set of well-known benchmark problems, standard IEEE CEC 2014 benchmark test and a recent set of benchmark problems, IEEE CEC 2017 have been taken. Several performance metrics (such as convergence, statistical test, performance index), employed on ISCA, ensure the robustness and efficiency of the algorithm. In the paper, the proposed algorithm ISCA is also used to solve five well-known engineering optimization problems. At the end of the paper, the proposed algorithm is also used for multilevel thresholding in image segmentation. The numerical experiments and analysis demonstrate that the proposed algorithm (ISCA) can be highly effective in solving real-life optimization problems. (C) 2018 Elsevier B.V. All rights reserved.