Modified shuffled frog-leaping algorithm (SFLA) applied to cutting stock problem /
Lacsamana, Margareth Ann Gallo
Modified shuffled frog-leaping algorithm (SFLA) applied to cutting stock problem / Margareth Ann Gallo Lacsamana. - 2008 - 90 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2008
This study focused on solving the one-dimensional variant of CSP with single stock length and without contiguity. In this study, an evolutionary algorithm (EA) called the shuffled frog-leaping algorithm (SFLA) was applied to the CSP. However, the local search of the SFLA was first modified using an operator from the genetic algorithm called the uniform crossover so s to use it in solving the CSP. The modified SFLA (MSLFA) with crossover was the only main algorithm used. The evaluation of the efficiency of the MSFLA was limited only to the chosen benchmark test problems. The experimental results in terms of the probability of convergence to a global optimal solution imply that the MSFLA can be an efficient tool for solving combinatorial optimization problems comparable to the EP. Generally, MSFLA provided equal results with EP in one of the benchmark test problems and an almost at par results with the EP in the other test problems with very little percentage error. Accordingly, MSFLA is a promising approach in solving the cutting stock problem if further improvements can be done.
Cutting stock problem.
Combinatorial optimization.
Optimization.
Discrete optimization.
Evolutionary algorithms.
Genetic algorithm.
Shuffled frog leaping algorithm.
Uniform crossover.
Undergraduate Thesis --CMSC200,
Modified shuffled frog-leaping algorithm (SFLA) applied to cutting stock problem / Margareth Ann Gallo Lacsamana. - 2008 - 90 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2008
This study focused on solving the one-dimensional variant of CSP with single stock length and without contiguity. In this study, an evolutionary algorithm (EA) called the shuffled frog-leaping algorithm (SFLA) was applied to the CSP. However, the local search of the SFLA was first modified using an operator from the genetic algorithm called the uniform crossover so s to use it in solving the CSP. The modified SFLA (MSLFA) with crossover was the only main algorithm used. The evaluation of the efficiency of the MSFLA was limited only to the chosen benchmark test problems. The experimental results in terms of the probability of convergence to a global optimal solution imply that the MSFLA can be an efficient tool for solving combinatorial optimization problems comparable to the EP. Generally, MSFLA provided equal results with EP in one of the benchmark test problems and an almost at par results with the EP in the other test problems with very little percentage error. Accordingly, MSFLA is a promising approach in solving the cutting stock problem if further improvements can be done.
Cutting stock problem.
Combinatorial optimization.
Optimization.
Discrete optimization.
Evolutionary algorithms.
Genetic algorithm.
Shuffled frog leaping algorithm.
Uniform crossover.
Undergraduate Thesis --CMSC200,