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http://bura.brunel.ac.uk/handle/2438/20290
Title: | Dynamic Impact for Ant Colony Optimization algorithm |
Authors: | Skackauskas, J Kalganova, T Dear, I Janakram, M |
Keywords: | ant colony optimization;dynamic Impact;scheduling;multi-dimensional knapsack problem;sparse data |
Issue Date: | 17-Oct-2021 |
Publisher: | Elsevier |
Citation: | Skackauskas, J., Kalganova, T., Dear, I. and Janakram, M. (2021) 'Dynamic Impact for Ant Colony Optimization algorithm' Swarm and Evolutionary Computation. 0 (in press), 100993. doi: 10.1016/j.swevo.2021.100993. |
Abstract: | This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Dynamic Impact is designed to solve challenging optimization problems that has nonlinear relationship between resource consumption and fitness in relation to other part of the optimized solution. This proposed method is tested against complex real-world Microchip Manufacturing Plant Production Floor Optimization (MMPPFO) problem, as well as theoretical benchmark Multi-Dimensional Knapsack problem (MKP). MMPPFO is a non-trivial optimization problem, due the nature of solution fitness value dependence on collection of wafer-lots without prioritization of any individual wafer-lot. Using Dynamic Impact on single objective optimization fitness value is improved by 33.2%. Furthermore, MKP benchmark instances of small complexity have been solved to 100% success rate where high degree of solution sparseness is observed, and large instances have showed average gap improved by 4.26 times. Algorithm implementation demonstrated superior performance across small and large datasets and sparse optimization problems. |
URI: | https://bura.brunel.ac.uk/handle/2438/20290 |
DOI: | https://doi.org/10.1016/j.swevo.2021.100993 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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