A hybrid approach to constrained global optimization |
| |
| Affiliation: | 1. College of Science, China University of Petroleum, Beijing 102249, China;2. Shanghai Development Center of Computer Software Technology, Shanghai, China;3. Australasian Joint Research Centre for Building Information Modelling, School of Built Environment, Curtin University, Perth, WA 6845, Australia;4. Department of Housing and Interior Design, Kyung Hee University, Seoul, Republic of Korea;5. Department of Mathematics, Curtin University, Perth, WA 6845, Australia;1. Manchester Business School, University of Manchester, Booth Street West, M15 6PB Manchester, UK;2. Department of Management Control and Information Systems, University of Chile, Av. Diagonal Paraguay 257, 8330015 Santiago, Chile;3. Department of Business Organization, Universitat Politècnica de València, Camino Vera s/n, 46022 Valencia, Spain;4. Department of Business Administration and Marketing, University of Sevilla, Av. Ramón y Cajal s/n, 41018 Sevilla, Spain;1. Institute of Material Physics, Key Laboratory of Display Materials and Photoelectric Devices, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China;2. School of Science, Tianjin University of Technology and Education, Tianjin 300222, China;1. REsearch Groups in Intelligent Machines (REGIM-Lab), University of Sfax, National School of Engineers (ENIS), BP 1173, Sfax 3038, Tunisia;2. Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, Czech Republic;3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, WA, USA,;1. Department of Actuarial Science and Applied Statistics, Faculty of Business & Information Science, UCSI University, Jalan Menara Gading, 56000 Cheras, Kuala Lumpur, Malaysia;2. Department of Mathematics, B.C. College, Asansol, West Bengal, India;3. School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia |
| |
| Abstract: | In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to. |
| |
| Keywords: | Global optimization Exact penalty Greedy Diffusion Search LBFGS method |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
