Abstract
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.
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References
Boettcher, S., Percus, A.: Extremal optimization: Methods derived from Co-evolution. In: Banzhaf, W., Daida, J., Eiben, A., Garzon, M., Honavar, V., Jakiela, M., Smith, R. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 825–832 (1999)
Boettcher, S., Percus, A.: Combining local search with co-evolution in a remarkably simple way. In: Proceedings of the Congress on Evolutionary Computation, Piscataway, NJ, pp. 1578–1584. IEEE Service Center (2000)
Boettcher, S., Percus, A.: Nature’s way of optimizing. Artificial Intelligence 119, 275–286 (2000)
Boettcher, S., Percus, A.: Extremal Optimization: An evolutionary local search algorithm. In: Proceedings of the 8th INFORMS Computer Society Conference, Norwell, MA. Interfaces in Computer Science and Operations Research, vol. 21, pp. 61–78. Kluwer Academic Publishers, Dordrecht (2003)
Chu, P., Beasley, J.: A genetic algorithm for the generalised assignment problem. Computers and Operations Research 24, 17–23 (1997)
Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, McGraw-Hill, London (1999)
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading MA (1989)
Kennedy, J., Eberhart, R.: The particle swam: Social adaptation in social information-processing systems. In: New Ideas in Optimization, pp. 379–387. McGraw-Hill, London (1999)
Lewis, A., Abramson, D., Peachey, T.: An evolutionary programming algorithm for automatic engineering design. In: Wyrzykowski, R., Dongarra, J.J., Paprzycki, M., Waśniewski, J. (eds.) Parallel Processing and Applied Mathematics. LNCS, vol. 3019, pp. 586–594. Springer, Heidelberg (2004)
Martello, S., Toth, P.: An algorithm for the generalised assignment problem. In: Proceedings of the 9th IFORS Conference, Hamburg, Germany (1981)
Moser, I., Hendtlass, T.: On the behaviour of extremal optimisation when solving problems with hidden dynamics. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, Springer, Heidelberg (2006)
Moser, I., Hendtlass, T.: Solving problems with hidden dynamics - comparison of extremal optimisation and ant colony system. In: Congress on Evolutionary Computing, pp. 1248–1255 (2006)
Randall, M.: Heuristics for ant colony optimisation using the generalised assignment problem. In: Proceedings of the Congress on Evolutionary Computing 2004, Portland, Oregon, pp. 1916–1923 (2004)
Randall, M.: Competitive ant colony optimisation. In: Okuno, H., Ali, M. (eds.) Twentieth International Conference on Industrial, Engineering and Other Applications of Applied Intelligence Systems. LNCS (LNAI), vol. 4570, pp. 974–983. Springer, Heidelberg (2007)
Randall, M., Lewis, A.: An extended extremal optimisation model for parallel architectures. In: 2nd IEEE International e-Science and Grid Computing Conference. Workshop on Biologically-inspired Optimisation Methods for Parallel and Distributed Architectures: Algorithms, Systems and Applications, IEEE Computer Society, Los Alamitos (2006)
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Randall, M. (2007). Enhancements to Extremal Optimisation for Generalised Assignment. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_32
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DOI: https://doi.org/10.1007/978-3-540-76931-6_32
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