AI Bibliography
AI Bibliography |
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Kao, Y.-T., & Zahara, E. (2008). A hybrid genetic algorithm and particle swarm optimization for multimodal functions. Applied soft computing, 8(2), 849–857. |
| Resource type: Journal Article BibTeX citation key: Kao2008 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General, Medical science Subcategories: Big data, Decision making, Deep learning, Informatics, Machine learning, Machine recognition Creators: Kao, Zahara Publisher: Collection: Applied soft computing |
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Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates.
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