Adaptive Coordination Ant Colony Optimization for Multipoint Dynamic Aggregation

Gao, Guanqiang; Mei, Yi; Jia, Ya-Hui; Browne, Will N.; Xin, Bin

doi:10.1109/tcyb.2020.3042511

Cited by 27 publications

(17 citation statements)

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“…To fair compare performances of different algorithms on the MO-MPDA problem, a comprehensive MO-MPDA benchmark set is developed in this paper. Following the characteristics of MPDA problem and VRPs [3], [16], [44], we transformed the single-objective MPDA benchmark set to a MO-MDPA benchmark set. The settings of instances of the MO-MPDA problem including the number of tasks, task initial demands and inherent increment rates follows the settings of the most comprehensive MPDA benchmark set in [16].…”

Section: Design Of Experiments a Data Setmentioning

confidence: 99%

“…There are some challenges in the MO-MPDA problem. First, due to the multi-objective characteristic, the previous single-objective MPDA optimisation approaches [3], [15], [16] cannot efficiently handle the two conflict objectives. When the number of used robots is changed, the whole single-objective approach needs to be adjusted to satisfy the changing robot number.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results showed that the method [14] outperforms the genetic algorithm and random search method. An adaptive coordination ant colony optimisation with an individual learning strategy is proposed to address the MPDA problem [16]. Experiments showed that the proposed ant colony optimisation algorithm significantly achieved a better performance than the state-of-the-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…When a robot completed a task, it becomes active and selects its next executing task according to the corresponding encoding. The details of the decoding method can be found in [3], [16]. Fig.…”

mentioning

confidence: 99%

“…AT = ∅, CT = ∅. 9: that, the states of robots and tasks are updated by Algorithm StateUpdate, whose details can be found in [3], [16]. Meanwhile, Algorithm StateUpdate returns the new set of active robots for the next round of the decision-making process.…”

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confidence: 99%

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A Hybrid Decomposition-based Multi-objective Evolutionary Algorithm for the Multi-Point Dynamic Aggregation Problem

Gao,

Xin,

Mei

et al. 2021

Preprint

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An emerging optimisation problem from the realworld applications, named the multi-point dynamic aggregation (MPDA) problem, has become one of the active research topics of the multi-robot system. This paper focuses on a multi-objective MPDA problem which is to design an execution plan of the robots to minimise the number of robots and the maximal completion time of all the tasks. The strongly-coupled relationships among robots and tasks, the redundancy of the MPDA encoding, and the variable-size decision space of the MO-MPDA problem posed extra challenges for addressing the problem effectively. To address the above issues, we develop a hybrid decompositionbased multi-objective evolutionary algorithm (HDMOEA) using ε-constraint method. It selects the maximal completion time of all tasks as the main objective, and converted the other objective into constraints. HDMOEA decomposes a MO-MPDA problem into a series of scalar constrained optimization subproblems by assigning each subproblem with an upper bound robot number. All the subproblems are optimized simultaneously with the transferring knowledge from other subproblems. Besides, we develop a hybrid population initialisation mechanism to enhance the quality of initial solutions, and a reproduction mechanism to transmit effective information and tackle the encoding redundancy. Experimental results show that the proposed HDMOEA method significantly outperforms the state-of-the-art methods in terms of several most-used metrics.

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