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An Accelerated Crow Search Algorithm for Multifactorial Optimization in Vehicle Routing Problem.

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Title: An Accelerated Crow Search Algorithm for Multifactorial Optimization in Vehicle Routing Problem.
Authors: Pratiwi, Asri Bekti, Wati, Claryta Putri Dedyana, Cahyana, Alfonsus Erhan, Damayanti, Auli, Winarko, Edi, Zamli, Kamal Z.
Source: Mathematical Modelling of Engineering Problems; Nov2025, Vol. 12 Issue 11, p4091-4101, 11p
Subject Terms: VEHICLE routing problem, EVOLUTIONARY algorithms, MATHEMATICAL optimization, EMPIRICAL research, MULTI-objective optimization
Abstract: This paper presents an efficient multifactorial evolutionary algorithm (MFEA), specifically the enhanced chaotic crow search (CS) algorithm, for addressing multifactorial optimization (MFO), particularly the variants of the Vehicle Routing Problem (VRP). The MFO framework involves multiple tasks, each defined within its own search space but all sharing a common representation. The multifactorial algorithm aims to efficiently obtain the optimal solution for each assigned task simultaneously within a single execution. Our proposed algorithm employs a chaotic map to enhance solution exploration by integrating it into the crow position updating process. Transfer learning is used to enhance the efficiency of learning characteristics across multiple tasks. Computational experiments were carried out to evaluate the algorithm's performance in terms of the best solution obtained and computational time, where multiple tasks are executed simultaneously and independently. Three models were used: the capacitated VRP, VRP with time windows, and VRP with simultaneous pickup and delivery. The findings indicate that increasing the population size generally leads to better objective function values, while more iterations also contribute to further improvement. Moreover, the optimal parameter configuration for multi-task optimization depends on the number of tasks being addressed. To ensure fairness and maintain optimal effectiveness, each additional task can be accompanied by a proportional increase in both the population size and the number of iterations. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:This paper presents an efficient multifactorial evolutionary algorithm (MFEA), specifically the enhanced chaotic crow search (CS) algorithm, for addressing multifactorial optimization (MFO), particularly the variants of the Vehicle Routing Problem (VRP). The MFO framework involves multiple tasks, each defined within its own search space but all sharing a common representation. The multifactorial algorithm aims to efficiently obtain the optimal solution for each assigned task simultaneously within a single execution. Our proposed algorithm employs a chaotic map to enhance solution exploration by integrating it into the crow position updating process. Transfer learning is used to enhance the efficiency of learning characteristics across multiple tasks. Computational experiments were carried out to evaluate the algorithm's performance in terms of the best solution obtained and computational time, where multiple tasks are executed simultaneously and independently. Three models were used: the capacitated VRP, VRP with time windows, and VRP with simultaneous pickup and delivery. The findings indicate that increasing the population size generally leads to better objective function values, while more iterations also contribute to further improvement. Moreover, the optimal parameter configuration for multi-task optimization depends on the number of tasks being addressed. To ensure fairness and maintain optimal effectiveness, each additional task can be accompanied by a proportional increase in both the population size and the number of iterations. [ABSTRACT FROM AUTHOR]
ISSN:23690739
DOI:10.18280/mmep.121135