TY - GEN
T1 - A multiobjective evolutionary algorithm for the 2D Guillotine Strip Packing Problem
AU - Coelho, Dayanne G.
AU - Wanner, Elizabeth F.
AU - Souza, Sergio R.
AU - Carrano, Eduardo G.
AU - Purshouse, Robin C.
PY - 2012/8/2
Y1 - 2012/8/2
N2 - This paper presents a specialized multiobjective evolutionary algorithm SPEA2 (Strength Pareto Evolutionary Algorithm 2) coupled, separetely, with four placement heuristics for solving the 2D Guillotine Strip Packing Problem. In this study, the problem requires minimization of both the amount of wasted material and the number of independent cuts required by a packing. With the goal of solving this multiobjective version of the problem, the construction phase of the GRASP algorithm (Greedy Randomized Adaptive Search Procedure) is used to generate a portion of the initial population of SPEA2. Four different placement heuristics, Next-Fit, a variation of Next-Fit, Best-Fit and First-Fit, were coupled with SPEA2 and were tested on a set of test data. The results show that the presented methodology is able to generate a good set of candidate solutions for each test problem. A statistical comparison methodology, based on multiobjective principles, was used to compare the four algorithm variants.
AB - This paper presents a specialized multiobjective evolutionary algorithm SPEA2 (Strength Pareto Evolutionary Algorithm 2) coupled, separetely, with four placement heuristics for solving the 2D Guillotine Strip Packing Problem. In this study, the problem requires minimization of both the amount of wasted material and the number of independent cuts required by a packing. With the goal of solving this multiobjective version of the problem, the construction phase of the GRASP algorithm (Greedy Randomized Adaptive Search Procedure) is used to generate a portion of the initial population of SPEA2. Four different placement heuristics, Next-Fit, a variation of Next-Fit, Best-Fit and First-Fit, were coupled with SPEA2 and were tested on a set of test data. The results show that the presented methodology is able to generate a good set of candidate solutions for each test problem. A statistical comparison methodology, based on multiobjective principles, was used to compare the four algorithm variants.
UR - https://ieeexplore.ieee.org/document/6256469
UR - https://www.scopus.com/pages/publications/84866858936
U2 - 10.1109/CEC.2012.6256469
DO - 10.1109/CEC.2012.6256469
M3 - Conference publication
AN - SCOPUS:84866858936
SN - 9781467315098
T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
Y2 - 10 June 2012 through 15 June 2012
ER -