Shuffled frog leaping-tabu search algorithm with grouping genetic algorithm operators applied on cutting stock problem (CSP) /
Ebero, Ria Theresa Magnaye.
Shuffled frog leaping-tabu search algorithm with grouping genetic algorithm operators applied on cutting stock problem (CSP) / Ria Theresa Magnaye Ebero. - 2011 - 106 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
This study was done to solve the one-dimensional variant of CSP with single stock length and without contiguity. The shuffled frog leaping algorithm (SFLA) was applied to CSP but was modified by embedding the tabu search (TS) in the SFLA's local search. The grouping genetic algorithm (GGA) operators were also used to address a constraint in SFLA. GGA crossover was used to create a new solution i SFLA while GGA mutation was used to create a new solution in TS. The efficiency of the algorithm was evaluated by using the chosen test data used by Lacsaman's (2008) study, namely the modified shuffled frog leaping algorithm (MSFLA). There were 8 parameter settings used in the study, 4 sets for SFLA and 2 sets for TS. The first parameter set of SFLA was from Amiri et al (2009) while the other set was generated by the proponent. The results showed that, generally, SFLA-TS performed better compared to MSFLA. It provided better results compared to MSFLA on test data 1,4 and 5, similar results with MSFLA in test data 2, and equal results on test data 3. Generally, SFLA-TS can be a promising solution in solving CSP.
Shuffled Frog Leaping Algorithm (SFLA)
Cutting Stock Problem CSP)
Tabu Search (TS)
Grouping Genetic Algorithm (GGA)
Hybrid algorithm.
Modified Shuffled Leaping Algorithm (MSFLA)
Undergraduate Thesis --CMSC200,
Shuffled frog leaping-tabu search algorithm with grouping genetic algorithm operators applied on cutting stock problem (CSP) / Ria Theresa Magnaye Ebero. - 2011 - 106 leaves.
Thesis (BS Computer Science) -- University of the Philippines Mindanao, 2011
This study was done to solve the one-dimensional variant of CSP with single stock length and without contiguity. The shuffled frog leaping algorithm (SFLA) was applied to CSP but was modified by embedding the tabu search (TS) in the SFLA's local search. The grouping genetic algorithm (GGA) operators were also used to address a constraint in SFLA. GGA crossover was used to create a new solution i SFLA while GGA mutation was used to create a new solution in TS. The efficiency of the algorithm was evaluated by using the chosen test data used by Lacsaman's (2008) study, namely the modified shuffled frog leaping algorithm (MSFLA). There were 8 parameter settings used in the study, 4 sets for SFLA and 2 sets for TS. The first parameter set of SFLA was from Amiri et al (2009) while the other set was generated by the proponent. The results showed that, generally, SFLA-TS performed better compared to MSFLA. It provided better results compared to MSFLA on test data 1,4 and 5, similar results with MSFLA in test data 2, and equal results on test data 3. Generally, SFLA-TS can be a promising solution in solving CSP.
Shuffled Frog Leaping Algorithm (SFLA)
Cutting Stock Problem CSP)
Tabu Search (TS)
Grouping Genetic Algorithm (GGA)
Hybrid algorithm.
Modified Shuffled Leaping Algorithm (MSFLA)
Undergraduate Thesis --CMSC200,