By Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger
This short offers a close creation, dialogue and bibliographic evaluation of the nature1-inspired optimization set of rules referred to as concord seek. It makes use of a number of simulation effects to illustrate the benefits of concord seek and its editions and in addition their drawbacks. The authors convey how weaknesses should be amended through hybridization with different optimization equipment. The concord seek procedure with functions could be of price to researchers in computational intelligence in demonstrating the cutting-edge of analysis on an set of rules of present curiosity. It additionally is helping researchers and practitioners of electric and machine engineering extra in general in acquainting themselves with this technique of vector-based optimization.
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Additional info for An Introduction to Harmony Search Optimization Method
5:40Þ Therefore, the task of the proposed hybrid HS optimization method is to optimize the membership functions in the above Sugeno fuzzy classification system by minimizing the objective function so that its data classification rate can be maximized. In the next section, the Fisher iris data and wine data are used as two representative test beds for examining this approach. 2 Simulations Fisher Iris Data Classification The Fisher iris data are a well-known challenging benchmark for the data classification techniques, which consist of four input measurements, sepal length (SL), sepal width (SW), petal length (PL), and petal width (PW), in 150 data sets .
Xn Þ is a single search point in the n-dimensional solution space, and xi 2 ½ai ; bi ; i = 1, 2,…, n. To simplify our presentation, only the continuous variables x are considered here. The opposition number xÃ ¼ À Ã Ã Á x1 ; x2 ; . ; xÃn of x ¼ ðx1 ; x2 ; . ; xn Þ is defined as follows: xÃi ¼ ai þ bi À xi ; i ¼ 1; 2; . ; n: ð5:14Þ The principle of the OBL for optimization is that the search for the optimal solutions should be on the basis of both x and x* as follows: In every iteration, x* is calculated from x, and let f(x) and f(x*) represent the fitness of x and x*, respectively.
References 1. Z. Gao, X. J. Ovaska, Uni-modal and multi-modal optimization using modified harmony search methods. Int. J. Innov. Comput. Inf. Control. 5(10a), 2985–2996 (2009) 2. M. Turkya, S. Abdullaha, A multi-population harmony search algorithm with external archive for dynamic optimization problems. Inf. Sci. 272(10), 84–95 (2014) References 29 3. O. Degertekin, Improved harmony search algorithms for sizing optimization of truss structures. Comput. Struct. 92–93, 229–241 (2012) 4. M. Mahdavi, M.