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An adaptive balance optimization algorithm and its engineering application |
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| Affiliation: | 1. College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Shandong, Qingdao 266590, China;2. College of Intelligent Equipment, Shandong University of Science and Technology, Shandong, Taian 271019, China;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;3. National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan 430074, China;1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing, PR China;2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, PR China;3. State Key Laboratory of Virtual Reality Technology and System, Beijing, PR China;1. ISAE-SUPMECA, Quartz Laboratory, Saint-Ouen, France;2. Roberval Laboratory, University of Technology of Compiègne, Compiègne, France;3. Laboratory of Mechanics of Sousse, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia;1. School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China;2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, PR China;1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China;2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China |
| Abstract: | The policy of balance between exploration capability and exploitation capability directly affects the solution performance of the meta-heuristic algorithm in a limited time. In order to better balance the exploration and exploitation capabilities of the algorithm and meet the solution requirements of complex real-world problems, the adaptive balance optimization algorithm (ABOA) is proposed in this paper. The algorithm consists of a global search phase (GSP) and a local search phase (LSP) and is controlled by a fixed parameter. ABOA not only considers the balance of exploration and exploitation capabilities of the algorithm throughout the whole iterative process but also focuses on the balance of exploration and exploitation in both GSP and LSP. The search in both phases is focused around the respective search centers from outside to inside. ABOA balances the exploration and exploitation capabilities of the algorithm throughout the search process by two adaptive policies: changing the search area and changing the search center. Fifty-two unconstrained benchmark test functions were employed to evaluate the performance of ABOA. The results of ABOA were compared with nine excellent optimization algorithms available in the literature. The statistical results and Friedman test showed that ABOA was significantly competitive. Finally, the results of the examined engineering design problems showed that ABOA can solve the constrained optimization problem better compared to other methods. |
| Keywords: | Adaptive balance optimization Exploration and exploitation Meta-heuristic algorithm Adaptive policies Engineering design problems |
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