Particles swarm optimization-simulated annealing (PSO-SA) with mass extinction applied to nonlinear optimization problems / Maria Andrea Aizza Galon Jopson.
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TextLanguage: English Publication details: 2009Description: 83 leavesSubject(s): Dissertation note: Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2009 Abstract: Particle Swarm Optimization ? Simulated Annealing (PSO-SA) with Mass Extinction is an extension of Xie's et.al.?s (2002), which searches for a hybrid technique that will get an optimal solution in numerical problems in evolutionary optimization research. PSO-SA with Mass Extinction is a combination of heuristic, meta-heuristic and evolutionary algorithms that aims to solve underlying problems in solving underlying problems in solving evolutionary problems. This hybrid algorithm will be tested three benchmark evolutionary functions namely; Rosenbrock function, Rastrigin function and Griewank function.
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University Library General Reference | Reference/Room-Use Only | LG993.5 2009 C6 J66 (Browse shelf(Opens below)) | Not For Loan | 3UPML00012507 | |
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University Library Archives and Records | Preservation Copy | LG993.5 2009 C6 J66 (Browse shelf(Opens below)) | Not For Loan | 3UPML00033162 |
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2009
Particle Swarm Optimization ? Simulated Annealing (PSO-SA) with Mass Extinction is an extension of Xie's et.al.?s (2002), which searches for a hybrid technique that will get an optimal solution in numerical problems in evolutionary optimization research. PSO-SA with Mass Extinction is a combination of heuristic, meta-heuristic and evolutionary algorithms that aims to solve underlying problems in solving underlying problems in solving evolutionary problems. This hybrid algorithm will be tested three benchmark evolutionary functions namely; Rosenbrock function, Rastrigin function and Griewank function.
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