### Abstract

Author:

Extremal optimisation in its canonical form is based on the manipulation of a single solution. This solution is changed iteratively by gradually replacing poor components of it so that over time it improves. Many successful evolutionary optimisers are population based, so it appears a reasonable exercise to extend extremal optimisation in this way. Scaling it up to an entire population presents many challenges, and only a few works have examined possible models. In this paper, a recent approach is expanded upon which extends the approach from assignment type problems (such as the generalised assignment problem) to permutation oriented ones. Using the asymmetric travelling salesman problem as a test case, it is found that improvements over a canonical extremal optimisation algorithm were realised.

Extremal optimisation in its canonical form is based on the manipulation of a single solution. This solution is changed iteratively by gradually replacing poor components of it so that over time it improves. Many successful evolutionary optimisers are population based, so it appears a reasonable exercise to extend extremal optimisation in this way. Scaling it up to an entire population presents many challenges, and only a few works have examined possible models. In this paper, a recent approach is expanded upon which extends the approach from assignment type problems (such as the generalised assignment problem) to permutation oriented ones. Using the asymmetric travelling salesman problem as a test case, it is found that improvements over a canonical extremal optimisation algorithm were realised.

Original language | English |
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Pages (from-to) | 38-43 |

Number of pages | 6 |

Journal | International Journal of Applied Mathematics and Informatics |

Volume | 11 |

Publication status | Published - 2017 |