A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems |
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| Affiliation: | 1. Soft Computing Laboratory, Computer Engineering and Information Technology Department, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran, Iran;2. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran;1. Department of Computing Technology at University of Alicante, Alicante, Spain;2. Department of Computational Sciences and Artificial Intelligence at University of Alicante, Alicante, Spain;1. Bialystok University of Technology, Faculty of Mechanical Engineering, ul. Wiejska 45C, 15-351 Bialystok, Poland;2. Center for Rotating Machinery Dynamics and Control, Cleveland State University, Department of Mechanical Engineering, Cleveland, OH 44115-2214, USA;1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China;2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China;3. School of Science and Technology, Middlesex University, London, UK;4. Human Spaceflight System Engineering Division, Institute of Manned Space System Engineering, CAST, Beijing, China;5. School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hong Kong;1. School of Mathematical Sciences, Fudan University, 200433, China;2. School of Statistics, Dongbei University of Finance and Economics, 116025, China;3. School of Mathematics and Statistics, Lanzhou University, 730000, China;1. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;2. School of Electronic Engineering, Dongguan University of Technology, Dongguan 523808, China;3. School of Engineering, Sun Yat-sen University, Guangzhou 510006, China |
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| Abstract: | This paper proposes a novel hybrid approach based on particle swarm optimization and local search, named PSOLS, for dynamic optimization problems. In the proposed approach, a swarm of particles with fuzzy social-only model is frequently applied to estimate the location of the peaks in the problem landscape. Upon convergence of the swarm to previously undetected positions in the search space, a local search agent (LSA) is created to exploit the respective region. Moreover, a density control mechanism is introduced to prevent too many LSAs crowding in the search space. Three adaptations to the basic approach are then proposed to manage the function evaluations in the way that are mostly allocated to the most promising areas of the search space. The first adapted algorithm, called HPSOLS, is aimed at improving PSOLS by stopping the local search in LSAs that are not contributing much to the search process. The second adapted, algorithm called CPSOLS, is a competitive algorithm which allocates extra function evaluations to the best performing LSA. The third adapted algorithm, called CHPSOLS, combines the fundamental ideas of HPSOLS and CPSOLS in a single algorithm. An extensive set of experiments is conducted on a variety of dynamic environments, generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the superiority of the proposed approach. |
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| Keywords: | Dynamic optimization problems Moving peaks benchmark DOPs MPB Particle swarm optimizer Naive direct search |
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