Lower bounds and reduction procedures for the bin packing problem
Discrete Applied Mathematics - Combinatorial Optimization
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Extremal optimization: heuristics via coevolutionary avalanches
Computing in Science and Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Differencing Method of Set Partitioning
The Differencing Method of Set Partitioning
Extremal Optimization with Local Search for the Circular Packing Problem
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Extremal Optimisation and Bin Packing
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Extremal Optimisation with a Penalty Approach for the Multidimensional Knapsack Problem
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications
Biologically-Inspired Optimisation Methods: Parallel Algorithms, Systems and Applications
Enhancements to extremal optimisation for generalised assignment
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
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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.