Extremal Optimisation with a Penalty Approach for the Multidimensional Knapsack Problem
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
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Biological systems are, by their very nature, adaptive. However, the meta-heuristic search algorithms inspired by them have mainly been applied to static problems (i.e., problems that do not change while they are being solved). Recently, a greater body of work has been completed on the newer meta-heuristics, particularly ant colony optimisation, particle swarm optimisation and extremal optimisation. This survey paper examines representative works and methodologies of these techniques on this class of problems. Beyond this we outline the limitations of these methods.