Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems

• URL: http://arxiv.org/abs/2304.07646v1
• Date: Sat, 15 Apr 2023 22:21:41 GMT
• Title: Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems
• Authors: Jonas Skackauskas, Tatiana Kalganova
• Abstract summary: Currently available dynamic optimization strategies for Ant Colony Optimization (ACO) algorithm offer a trade-off of slower algorithm convergence or significant penalty to solution quality after each dynamic change occurs. This paper proposes a discrete dynamic optimization strategy called Ant Colony Optimization (ACO) with Aphids, modelled after a real-world symbiotic relationship between ants and aphids.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Currently available dynamic optimization strategies for Ant Colony Optimization (ACO) algorithm offer a trade-off of slower algorithm convergence or significant penalty to solution quality after each dynamic change occurs. This paper proposes a discrete dynamic optimization strategy called Ant Colony Optimization (ACO) with Aphids, modelled after a real-world symbiotic relationship between ants and aphids. ACO with Aphids strategy is designed to improve solution quality of discrete domain Dynamic Optimization Problems (DOPs) with event-triggered discrete dynamism. The proposed strategy aims to improve the inter-state convergence rate throughout the entire dynamic optimization. It does so by minimizing the fitness penalty and maximizing the convergence speed that occurs after the dynamic change. This strategy is tested against Full-Restart and Pheromone-Sharing strategies implemented on the same ACO core algorithm solving Dynamic Multidimensional Knapsack Problem (DMKP) benchmarks. ACO with Aphids has demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2%. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5%.

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