Dynamic particle swarm optimizer with escaping prey for solving constrained non-convex and piecewise optimization problems |
| |
| Affiliation: | 1. School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China;2. Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK;3. Department of Physics, University of Liverpool, Liverpool L69 7ZE, UK;1. Department of Industrial Engineering, Istanbul Commerce University, Küçükyal? E5 Kav?a?? ?nönü Cad. No: 4, Küçükyal? 34840, Istanbul, Turkey;2. Istanbul Medeniyet University Faculty of Engineering and Natural Sciences, Department of Industrial Engineering 34700 Üsküdar, Istanbul, Turkey;1. Department of Computer Engineering, Omer Halisdemir University, 51245 Nigde, Turkey;2. Department of Computer Engineering, Erciyes University, 38039 Kayseri, Turkey;1. International Business School, Shaanxi Normal University, Xi’an 710119, China;2. School of Economics & Management, China University of Petroleum, Qingdao 266580, China;3. School of Management, Wuhan University of Technology, Wuhan 430070, China;4. Department of Electrical & Computer Engineering, University of Alberta, Alberta T6G 2R3, Canada;5. LeBow College of Business, Drexel University, Philadelphia, PA 19104, USA;6. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;7. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Department of Business Administration, Hansung University, Seoul, South Korea;2. Department of Business Administration, Seoul National University, Seoul, South Korea;3. Department of Business Administration, Sangji Youngseo College, 660 Usan-dong, Wonju-si, Gangwon-do 26339, South Korea\n |
| |
| Abstract: | This paper presents a novel meta-heuristic algorithm, dynamic particle swarm optimizer with escaping prey (DPSOEP), for solving constrained non-convex and piecewise optimization problems. In DPSOEP, the particles developed from two different species are classified into three different types, consisting of preys, strong particles and weak particles, to simulate the behavior of hunting and escaping characteristics observed in nature. Compared to other variants of particle swarm optimizer (PSO), the proposed algorithm takes account of an escaping mechanism for the preys to circumvent the problem of local optimum and also develops a classification mechanism to cope with different situations in the search space so as to achieve a good balance between its global exploration and local exploitation abilities. Simulation results obtained based on thirteen benchmark functions and two practical economic dispatch problems prove the effectiveness and applicability of the DPSOEP to deal with non-convex and piecewise optimization problem, considering the integration of linear equality and inequality constraints. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
