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- W4387005616 abstract "Evolutionary multitasking (EMT), which aims to exploit effective knowledge among similar tasks to improve search efficiency, is a hot research topic that has recently attracted a lot of attention. Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ant species, is a popular and powerful search algorithm for NP-hard combinatorial optimization problems. However, EMT has seldom been integrated with the ACO. Inspired by the remarkable success of multitasking evolutionary algorithms in numerous research fields, this paper proposes a multitasking ant colony optimization framework (MTACO). The proposed framework enables ants to exploit the pheromones of ant colonies with similar tasks through certain conditions to improve the efficiency and the quality of results of ACO when processing multiple similar tasks dynamically. Furthermore, a multitasking ACS (MTACS) is implemented based on the proposed MTACO framework to solve dynamic vehicle path planning problems (DVPP). The experimental results on DVPP have verified that MTACO can improve the performance of ACO in terms of both algorithm efficiency and quality of results, when ACO is handling multiple tasks simultaneously." @default.
- W4387005616 created "2023-09-26" @default.
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- W4387005616 date "2023-07-01" @default.
- W4387005616 modified "2023-09-26" @default.
- W4387005616 title "An Efficient Multitasking Ant Colony Optimization Framework" @default.
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- W4387005616 doi "https://doi.org/10.1109/cec53210.2023.10254137" @default.
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