A hybrid shuffled frog-leaping with harmony search (SFL-HS) algorithm applied to continuous benchmark optimization problems /
Alsado, Peter Raff Bulat-ag.
A hybrid shuffled frog-leaping with harmony search (SFL-HS) algorithm applied to continuous benchmark optimization problems / Peter Raff Bulat-ag Alsado. - 2011 - 98 leaves
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
Many real life problems are often formulated as continuous optimization problems. There are several methods reported in literature that can solve many kinds of continuous optimization problems. Evolutionary algorithms have been used to solve these kinds of problems and Shuffled Frog-Leaping Algorithm (SFLA) was one that showed great potential and capability in solving near-optimum solutions to large scale optimization problems. With the trend of hybrid algorithms to create a new and more efficient algorithm, heuristic algorithms were embedded with meta-heuristics to improve the pure algorithm itself. This study developed a hybrid algorithm by combining SFLA and Harmony Search (HS), a meta-heuristic algorithm, to solve two continuous optimization problems; the f8 (Griewank) and ef10(extended f10) functions. The results proved to be worse as compared to the ones obtained by the study of Elbeltagi et al.(2005). However, initial experimentation which regards to the parameters used by the previous ones especially in solving the f8 function in terms of mean fitness and percentage of success. Also, the processing time obtained for the SFL-HS algorithm dominated the ones achieved by the algorithms it was compared to. In relation to this, SFL-HS demonstrated its capability to approach the optimum solution rapidly especially during the earlier generations. A more extensive experimentation for the best parameter settings for SFL-HS could show great promise of producing better results.
Continuous optimization.
Optimization.
Harmony Search (HS)
Heuristics.
Hybrid algorithms.
Meta-heuristics.
Shuffle-Frog-Leaping Algorithm (SFLA)
Benchmark optimization.
Evolutionary algorithms.
Undergraduate Thesis --CMSC200,
A hybrid shuffled frog-leaping with harmony search (SFL-HS) algorithm applied to continuous benchmark optimization problems / Peter Raff Bulat-ag Alsado. - 2011 - 98 leaves
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
Many real life problems are often formulated as continuous optimization problems. There are several methods reported in literature that can solve many kinds of continuous optimization problems. Evolutionary algorithms have been used to solve these kinds of problems and Shuffled Frog-Leaping Algorithm (SFLA) was one that showed great potential and capability in solving near-optimum solutions to large scale optimization problems. With the trend of hybrid algorithms to create a new and more efficient algorithm, heuristic algorithms were embedded with meta-heuristics to improve the pure algorithm itself. This study developed a hybrid algorithm by combining SFLA and Harmony Search (HS), a meta-heuristic algorithm, to solve two continuous optimization problems; the f8 (Griewank) and ef10(extended f10) functions. The results proved to be worse as compared to the ones obtained by the study of Elbeltagi et al.(2005). However, initial experimentation which regards to the parameters used by the previous ones especially in solving the f8 function in terms of mean fitness and percentage of success. Also, the processing time obtained for the SFL-HS algorithm dominated the ones achieved by the algorithms it was compared to. In relation to this, SFL-HS demonstrated its capability to approach the optimum solution rapidly especially during the earlier generations. A more extensive experimentation for the best parameter settings for SFL-HS could show great promise of producing better results.
Continuous optimization.
Optimization.
Harmony Search (HS)
Heuristics.
Hybrid algorithms.
Meta-heuristics.
Shuffle-Frog-Leaping Algorithm (SFLA)
Benchmark optimization.
Evolutionary algorithms.
Undergraduate Thesis --CMSC200,