000			02351nam a22002533a 4500
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003			UPMIN
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008			221111b |||||||| |||| 00| 0 eng d
040			_aDLC _cDLC _dupmin
041			_aeng
090		0	_aLG993.5 2006 _bC6 T44
100			_aSimogan, Bejay Parparan.
245			_aHybrid particle swarm optimization-simulated annealing (PSO-SA) approach applied to constrained engineering optimization problems / _cBejay P. Simogan
260			_c2006
300			_a88 leaves.
502			_aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2006
520	3		_aConstraint handling is considered one of the most complicated parts of engineering design optimization. Real-world limitations frequently introduce multiple, non-linear and non-trivial constraints on a design. Due to this complexity and unpredictability, a general deterministic solution is hard to find. In recent years, several evolutionary algorithms, search techniques, heuristic and meta-heuristic methods have been proposed for constrained engineering optimization problems. Hu et.al. (2003) used Particle Swarm optimization (PSO) in solving such problems. However, despite the good results, they also found out some limitations to the study. To avoid those restrictions and create a more efficient algorithm that would still generate favorable results, this paper presents an embedded hybrid of PSO and Simulated Annealing (SA) for solving engineering optimization problems. PSO is a heuristic type of algorithm that generate solutions which are near optimal while SA is a generic probabilistic meta-algorithm for the global optimization problems, namely locating a good approximation to the global optimum of a given function in a large search space. Four benchmark engineering problems with constraints were tested namely, (1) pressure vessel design problem, (2) welded beam design problem, (3) minimization of the weight of the tension/compression spring, and (4) Himmelblau?s nonlinear optimization n problems. The best solution of the above-mentioned method is better compared to all other algorithms previously reported in the literature
650	1	7	_aParticle swarm optimization.
658			_aUndergraduate Thesis _cCMSC200, _2BSCS
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c690 _d690