Parallel chaos optimization algorithm with migration and merging operation |
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| Affiliation: | 1. Dipartimento Biomedico di Medicina Interna e Specialistica, UOC Ematologia con Trapianto, Università di Palermo, Palermo, Italy;2. Laboratorio Centralizzato Policlinico “P.Giaccone”, Sezione Emostasi e Trombosi, Palermo, Italy;3. Unità di Medicina Trasfusionale ed Immunoematologia, Ospedale “Civico” Palermo, Italy;4. Laboratorio di Ricerca CLADIBIOR, Università di Palermo, Palermo, Italy;5. Promedical Company, Italy;6. Istituto di Statistica, ISTAT, Palermo, Italy;1. Department of Mechanical Engineering, Universiti Teknologi Petronas, Malaysia;2. Department Civil Engineering, Universiti Teknologi Petronas, Malaysia;1. Department of Information Systems Management and GREC Group, Esade – Universitat Ramon Llull, Barcelona, Spain;2. Department of Neonatology, Máxima Medical Center, The Netherlands;3. Department of Applied Mathematics 2 and GREC Group, Technical University of Catalonia, UPC-BarcelonaTech, Barcelona, Spain;1. Information Systems Department, ESADE Business School, Ramon Llull University, Av. Pedralbes, 60-62, Barcelona, Spain;2. Data Science and Operations Department, Marshall School of Business, University of Southern California, Los Angeles, USA;1. School of Electrical & Automatic Engineering, Changshu Institute of Technology, 215500 Changshu, China;2. School of Automation, Nanjing University of Science & Technology, 210094 Nanjing, China;1. Department of Computer Science, Pondicherry University, Pondicherry, India;2. Department of ECE/MIT, Pondicherry, India;3. Department of Computer Science, RGET, Pondicherry, India |
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| Abstract: | Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm. |
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| Keywords: | Optimization algorithm Chaos optimization algorithm (COA) Parallel chaos optimization algorithm (PCOA) Parameter identification |
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