Sine and cosine in matlab 2017
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Secondly, harmony search part starts its search from SCA finds so far to augment the exploitation tendencies. The new approach presents own work performance in two different stages firstly, the sine–cosine algorithm starts the explore procedure to augment exploration capability. The existing approach integrates the merits of the sine–cosine algorithm and HS algorithm to reduce demerits, like the trapping in local optima and unbalanced exploitation. To overcome these shortcomings in this work, we are trying to present a new heuristic approach based on merging the features of SCA (sine–cosine approach) with a HS (harmony search approach) known as HSCAHS algorithm. The search process of the SCA method holds various shortcomings such as slow convergence, weak balance amid exploration and exploitation, and inefficiency in convergence. Nevertheless, standard SCA provides insufficient global optima results on complex dimension functions illustrating poor convergence rate. Sine–cosine optimizer is a stochastic technique that generates various preliminary random research agents global optimal solutions and involves them to fluctuate toward or outwards the superior global optima solution utilizing a mathematical model based on sine and cosine trigonometry functions. Recent developments designate the quick growing of optimization meta-heuristics in the domain of optimization.