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A new genetic algorithm for global optimization of multimodal continuous functions |
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| Affiliation: | 1. South Asian University, New Delhi, India;2. ABV-Indian Institute of Information Technology and Management, Gwalior, India;1. School of Computer Sciences, Universiti Sains Malaysia, 11800 Penang, Malaysia;2. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China;3. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool L16 9JD, UK;1. College of Information Science and Technology, University of Nebraska at Omaha, USA;2. Chinese Academy of Sciences, Beijing, China |
| Abstract: | In this paper a new genetic algorithm is developed to find the near global optimal solution of multimodal nonlinear optimization problems. The algorithm defined makes use of a real encoded crossover and mutation operator. The performance of GA is tested on a set of twenty-seven nonlinear global optimization test problems of variable difficulty level. Results are compared with some well established popular GAs existing in the literature. It is observed that the algorithm defined performs significantly better than the existing ones. |
| Keywords: | Genetic algorithms Global optimization Pareto crossover Power mutation |
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