Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2010

Many real problems are often formulated as constrained optimization problems. There are several methods reported i literature that can solve many kinds of constrained optimization problems. Genetic algorithms and other Evolutionary algorithms have been used to solve these kinds of problems. Particle Swarm Optimization was one that has been an effective method reported in literature to solve optimization problems. With the advent of hybrid algorithms to create a new and more efficient algorithm, Genetic algorithms, Evolutionary algorithms, and other sorts of heuristics was embedded with metaheuristics to improve the pure algorithm itself. Hence, a meta-heuristic Harmony Search (HS) was introduced to the pure PSO to solve constrained optimization problems. With the hybrid, the study showed positive results compared to the pure PSO algorithm though it has quite a difference3 in terms of running time. It is well known that constrained function optimization involves multiple, nonlinear and non-trivial constraints due to real world limitations. But from a constrained optimization standpoint, running time is usually expected to be quite high but a better solution is always desired. With the hybrid it has found a better solution compared to the pure PSO regardless of the running time.