Particle swarm optimization - tabu search (PSO-TS) with mass extinction applied to nonlinear optimization problems /
Yutiamco, Crystal Dianne C.
Particle swarm optimization - tabu search (PSO-TS) with mass extinction applied to nonlinear optimization problems / Crystal Dianne C. Yutiamco - 2010 - 101 leaves
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2010
This study serves an attempt to improve the solutions acquired by the hybrid PSO with Mass Extinction by incorporating another metaheuristic called tabu search. This study also serves as an additional contribution to several scientific fields and may lead to another research in the future. PSO-TS with Mass Extinction is an algorithm which aims to get an optimal solution for the Rosenbrock, Rastigin and Griewank function. The parameter setting s were divided to three groups according to the particle dimensions for all the benchmark problems and the average solution quality, average solution time, best solution quality and best solution time were determined for each problem. The results for PSO-TS with Mass Extinction were also compared with those of PSO with Mass Extinction using charts and tables. For most cases, PSO-TS with Mass Extinction outperforms PSO with Mass Extinction in terms of the average solution quality and best solution quality. However, it took a longer time for PSO-TS with Mass Extinction to achieve a solution as compared to PSO with Mass Extinction since PSO-TS with Mass Extinction required additional processes for the Tabu Search Algorithm
Rosenbrock function
Rastrigin function
Griewank function
Particle swarm optimization (PSO)
Tabu search
Mass extinction
Particle swarm optimization=tabu search with mass extinction
Undergraduate Thesis --CMSC200,
Particle swarm optimization - tabu search (PSO-TS) with mass extinction applied to nonlinear optimization problems / Crystal Dianne C. Yutiamco - 2010 - 101 leaves
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2010
This study serves an attempt to improve the solutions acquired by the hybrid PSO with Mass Extinction by incorporating another metaheuristic called tabu search. This study also serves as an additional contribution to several scientific fields and may lead to another research in the future. PSO-TS with Mass Extinction is an algorithm which aims to get an optimal solution for the Rosenbrock, Rastigin and Griewank function. The parameter setting s were divided to three groups according to the particle dimensions for all the benchmark problems and the average solution quality, average solution time, best solution quality and best solution time were determined for each problem. The results for PSO-TS with Mass Extinction were also compared with those of PSO with Mass Extinction using charts and tables. For most cases, PSO-TS with Mass Extinction outperforms PSO with Mass Extinction in terms of the average solution quality and best solution quality. However, it took a longer time for PSO-TS with Mass Extinction to achieve a solution as compared to PSO with Mass Extinction since PSO-TS with Mass Extinction required additional processes for the Tabu Search Algorithm
Rosenbrock function
Rastrigin function
Griewank function
Particle swarm optimization (PSO)
Tabu search
Mass extinction
Particle swarm optimization=tabu search with mass extinction
Undergraduate Thesis --CMSC200,