By Xiaolei Wang, Xiao-Zhi Gao, Kai Zenger
This short offers an in depth advent, dialogue and bibliographic evaluation of the nature1-inspired optimization set of rules known as concord seek. It makes use of a great number of simulation effects to illustrate the benefits of concord seek and its variations and in addition their drawbacks. The authors exhibit how weaknesses may be amended via hybridization with different optimization tools. The concord seek procedure with functions can be of worth to researchers in computational intelligence in demonstrating the state-of-the-art of study on an set of rules of present curiosity. It additionally is helping researchers and practitioners of electric and laptop engineering extra normally in acquainting themselves with this technique of vector-based optimization.
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Extra info for An Introduction to Harmony Search Optimization Method
N = 20, and 118 sets of wine data are used as the test data. As we can observe, the hybrid HS method yields the best classification performance. As an illustrative example, the initial membership functions of flavanoids and color intensity in the simplified Sugeno fuzzy classification system are demonstrated in Fig. 17. 18 shows the optimized membership functions of Small, Medium, and Large. The following three rules are utilized: 1. IF Mal is Small and Tot is Large and Fla is Large and Col is Medium and Hue is Large and OD2 is Large and Pro is Large, THEN wine is Class 1.
A modified differential evolution for constrained optimization. ICIC Express Lett. 2(2), 181–186 (2008) 23. C. Coello, Use of a self-adaptive penalty approach for engineering optimization problems. Comput. Ind. 41(2), 113–127 (2000) 24. X. Wang, X. Yan, Global best harmony search algorithm with control parameters coevolution based on PSO and its application to constrained optimal problems. Appl. Math. Comput. 219(19), 10059–10072 (2013) 25. N. Sinsuphan, U. Leeton, T. Kulworawanichpong, Optimal power flow solution using improved harmony search method.
X1n , ~ ; xHMS ; . ; x ¼ x1 ; x2 ; . ; x2n , …, ~ xHMS ¼ xHMS 1 2 HMS xn . The HM members are divided into two different parts: feasible members and infeasible members, as illustrated in Fig. 2. The former satisfy all the constraint functions, while the latter do not. Thus, the ranking of the HM members is separated into two consecutive stages: ranking of the feasible HM members and ranking of the infeasible ones. The ranking of the feasible HM members is straightforward: They can be sorted using their objective functions values.