Dynamic impact for ant colony optimization algorithm
نویسندگان
چکیده
This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Impact is designed to improve convergence and solution quality solving challenging optimization problems that have a non-linear relationship between resource consumption fitness. proposed tested against the real-world Microchip Manufacturing Plant Production Floor (MMPPFO) problem theoretical benchmark Multidimensional Knapsack (MKP). Using on single-objective fitness value improved by 33.2% over ACO without Furthermore, MKP instances of low complexity been solved 100% success rate even when high degree sparseness observed. Large shown average gap 4.26 times.
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ژورنال
عنوان ژورنال: Swarm and evolutionary computation
سال: 2022
ISSN: ['2210-6502', '2210-6510']
DOI: https://doi.org/10.1016/j.swevo.2021.100993