Hybrid particle swarm optimization tabu-search (PSO-TS) approach applied to constrained engineering optimization problems /
Mesa, Armacheska Rivero.
Hybrid particle swarm optimization tabu-search (PSO-TS) approach applied to constrained engineering optimization problems / Armacheska R. Mesa - 2007 - 69 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2007
Many engineering design problems can be formulated as constrained optimization problems. There are several methods reported in literature that can solve many of these optimization design problems with constraints. Genetic algorithm, self-adaptive penalty approach and other evolutionary algorithms had been used to find the optimal solutions to these engineering problems. So far, particle swarm optimization has been the most effective method reported in literature to solve such problems. With the advent of the hybridization techniques to create efficient algorithms pure PSO was paired to several other heuristics and these hybrids were used to solve many optimization problems. Hence, a fast, intelligent meta-heuristic, Tabu Search (TS), was introduced to the pure PSO to solve engineering optimization problems. With the embedded hybridization, the study showed positive results returned by the PSO-TS hybrids and were better compared to the results of other algorithms reported in Hu et.al?s. (2003) and He and Wang?s paper (2006). It is well known that practical engineering optimization involves multiple, nonlinear and non-trivial constraints due to real world limitations. From an engineering standpoint, a better, faster, cheaper solution is always desired. In this study, the embedded hybrid performed well on all our engineering optimization problems tested
Optimization.
Undergraduate Thesis --CMSC200,
Hybrid particle swarm optimization tabu-search (PSO-TS) approach applied to constrained engineering optimization problems / Armacheska R. Mesa - 2007 - 69 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2007
Many engineering design problems can be formulated as constrained optimization problems. There are several methods reported in literature that can solve many of these optimization design problems with constraints. Genetic algorithm, self-adaptive penalty approach and other evolutionary algorithms had been used to find the optimal solutions to these engineering problems. So far, particle swarm optimization has been the most effective method reported in literature to solve such problems. With the advent of the hybridization techniques to create efficient algorithms pure PSO was paired to several other heuristics and these hybrids were used to solve many optimization problems. Hence, a fast, intelligent meta-heuristic, Tabu Search (TS), was introduced to the pure PSO to solve engineering optimization problems. With the embedded hybridization, the study showed positive results returned by the PSO-TS hybrids and were better compared to the results of other algorithms reported in Hu et.al?s. (2003) and He and Wang?s paper (2006). It is well known that practical engineering optimization involves multiple, nonlinear and non-trivial constraints due to real world limitations. From an engineering standpoint, a better, faster, cheaper solution is always desired. In this study, the embedded hybrid performed well on all our engineering optimization problems tested
Optimization.
Undergraduate Thesis --CMSC200,