TY - GEN
T1 - A hybrid extremal optimisation approach for the bin packing problem
AU - Gómez-Meneses, Pedro
AU - Randall, Marcus
PY - 2009
Y1 - 2009
N2 - Extremal optimisation (EO) is a simple and effective technique that is influenced by nature and which is especially suitable to solve assignment type problems. EO uses the principle of eliminating the weakest or the least adapted component and replacing it by a random one. This paper presents a new hybrid EO approach that consists of an EO framework with an improved local search for the bin packing problem (BPP). The stochastic nature of the EO framework allows the solution to move between feasible and infeasible spaces. Hence the solution has the possibility of escaping from a stagnant position to explore new feasible regions. The exploration of a feasible space is complemented with an improved local search mechanism developed on the basis of the proposed Falkenauer's technique. The new local search procedure increases the probability of finding better solutions. The results show that the new algorithm is able to obtain optimal and efficient results for large problems when the approach is compared with the best known methods.
AB - Extremal optimisation (EO) is a simple and effective technique that is influenced by nature and which is especially suitable to solve assignment type problems. EO uses the principle of eliminating the weakest or the least adapted component and replacing it by a random one. This paper presents a new hybrid EO approach that consists of an EO framework with an improved local search for the bin packing problem (BPP). The stochastic nature of the EO framework allows the solution to move between feasible and infeasible spaces. Hence the solution has the possibility of escaping from a stagnant position to explore new feasible regions. The exploration of a feasible space is complemented with an improved local search mechanism developed on the basis of the proposed Falkenauer's technique. The new local search procedure increases the probability of finding better solutions. The results show that the new algorithm is able to obtain optimal and efficient results for large problems when the approach is compared with the best known methods.
UR - http://www.scopus.com/inward/record.url?scp=78650447532&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10427-5_24
DO - 10.1007/978-3-642-10427-5_24
M3 - Conference contribution
AN - SCOPUS:78650447532
SN - 3642104266
SN - 9783642104268
VL - 5865 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 242
EP - 251
BT - Artificial Life: Borrowing from Biology - 4th Australian Conference, ACAL 2009, Proceedings
T2 - 4th Australian Conference on Artificial Life: Borrowing from Biology, ACAL 2009
Y2 - 1 December 2009 through 4 December 2009
ER -