Abstract
In recent years, there has been an increasing interest in Opposite Learning strategies. In this work, we propose COISA, a Cooperative Opposite-Inspired Strategy for Ants. Inspired on the concept of anti-pheromone, in this approach, sub-colonies of ants perform different search processes to construct an initial pheromone matrix. We aim to produce a repel effect to (temporarily) avoid components that were related to an undesirable characteristic. To assess the effectiveness of COISA, we selected Ant Knapsack, a well-known ant-based algorithm that efficiently solves the Multidimensional Knapsack Problem. Results in benchmark instances show that the performance of Ant Knapsack is improved considering the opposite information, so that it can reach better solutions than before.
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Notes
- 1.
Considering that \(\mathbb {P}\) is a maximization problem with an objective function F and \(I_C^*\) is an optimal solution.
- 2.
These resources can be execution time, a fixed number of evaluations, and conflict checks, among others. In general, the amount of resources can be defined considering how A was originally evaluated.
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Acknowledgements
Second author is partially supported by the Centro Científico Tecnológico de Valparaíso (CCTVal) Project No. FB0821. The first author is supported by CONICYT-PCHA/National Doctorate/2015-21150696. Third author acknowledges support from CONACyT project no. 221551.
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Rojas-Morales, N., Riff, MC., Coello Coello, C.A., Montero, E. (2018). A Cooperative Opposite-Inspired Learning Strategy for Ant-Based Algorithms. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A., Reina, A., Trianni, V. (eds) Swarm Intelligence. ANTS 2018. Lecture Notes in Computer Science(), vol 11172. Springer, Cham. https://doi.org/10.1007/978-3-030-00533-7_25
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