A genetic algorithm for the generalised assignment problem
Computers and Operations Research
The ant colony optimization meta-heuristic
New ideas in optimization
The particle swarm: social adaptation in information-processing systems
New ideas in optimization
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Extended Extremal Optimisation Model for Parallel Architectures
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Competitive ant colony optimisation
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
On the behaviour of extremal optimisation when solving problems with hidden dynamics
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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
A Hybrid Extremal Optimisation Approach for the Bin Packing Problem
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Intensification strategies for extremal optimisation
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Differential evolution for a constrained combinatorial optimisation problem
International Journal of Metaheuristics
A Novel Extremal Optimization Approach for the Template Design Problem
International Journal of Organizational and Collective Intelligence
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Extremal optimisation (EO) is a relatively new meta-heuristic technique that is based on the principles of self organising criticality. It allows for a poorly performing solution component to be removed at each iteration of the algorithm and be replaced by a random one. Over time, improvements emerge and the system is driven towards good quality solutions. There has been very little literature concerning EO and combinatorial optimisation and relatively few computational results have been reported. In this paper, an enhanced model of EO, which allows the traversal feasible and infeasible spaces, is presented. This improved version is able to operate on single solutions as well as populations of solutions. In addition to local search, a simple partial feasibility restoration heuristic is introduced. The computational results for the generalised assignment problem indicate that it provides significantly better quality solutions over a sophisticated ant colony optimisation implementation.