A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation |
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| Affiliation: | 1. CEFET-RJ, Brazil;2. PROSAICO – PEL/DETEL – UERJ, Brazil;3. PEE/COPPE/DEL/Poli, UFRJ, Brazil;1. Instituto Superior Técnico, Universidade de Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;2. INESC-ID Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal;1. Institute of Computing, University of Campinas, SP, Brazil;2. Dept. of Computer Engineering, Federal Technological University of Parana, PR, Brazil;3. IMMUNOCAMP Research and Development of Technology, SP, Brazil;4. Institute of Biology, University of Campinas, SP, Brazil;1. Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milan, Italy;2. Systems Science and the Energetic Challenge, European Foundation for New Energy-Electricité de France, Ecole Centrale Paris and Supelec, Paris, 92295 Chatenay-Malabry Cedex, France;3. Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, UK;1. Institute for Development & Research in Banking Technology, Hyderabad, India;2. School of Computer & Information Science, University of Hyderabad, Hyderabad, India |
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| Abstract: | Metaheuristic optimization algorithms have become a popular choice for solving complex problems which are otherwise difficult to solve by traditional methods. However, these methods have the problem of the parameter adaptation and many researchers have proposed modifications using fuzzy logic to solve this problem and obtain better results than the original methods. In this study a comprehensive review is made of the optimization techniques in which fuzzy logic is used to dynamically adapt some important parameters in these methods. In this paper, the survey mainly covers the optimization methods of Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Ant Colony Optimization (ACO), which in the last years have been used with fuzzy logic to improve the performance of the optimization methods. |
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| Keywords: | Particle Swarm Optimization Gravitational Search Algorithm Ant Colony Optimization Fuzzy logic |
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