000			02155nam a22002533a 4500
001			UPMIN-00000518099
003			UPMIN
005			20221020160208.0
008			221020b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG993.5 2007 _bC6 M48
100			_aMesa, Armacheska Rivero.
245			_aHybrid particle swarm optimization tabu-search (PSO-TS) approach applied to constrained engineering optimization problems / _cArmacheska R. Mesa
260			_c2007
300			_a69 leaves.
502			_aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2007
520	3		_aMany 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
650	1	7	_aOptimization.
658			_aUndergraduate Thesis _cCMSC200, _2BSCS
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c688 _d688