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A New Multi-Objective Ant Colony Optimisation Algorithm for Solving the Quadratic Assignment Problem

Authors:

I. D. I. D. Ariyasingha ,

Open University of Sri Lanka, LK
About I. D. I. D.
Department of Computer Science, Faculty of Natural Sciences
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T. G. I. Fernando

University of Sri Jayewardenepura, LK
About T. G. I.
Department of Computer Science, Faculty of Applied Sciences
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Abstract

The multi-objective quadratic assignment problem (mQAP) is an NP-hard combinatorial optimisation problem. Real world problems are concerned with multi-objective problems which optimise more objective functions simultaneously. Moreover, QAP models many real-world optimisation problems, such as network design problems, communication problems, layout problems, etc. One of its major applications is the facility location, which is to find an assignment of all facilities to all locations in the way their total is minimised. The multi-objective QAP considers multiple types of flows between two facilities. Over the last few decades several meta-heuristic algorithms have been proposed to solve the multi-objective QAP, such as genetic algorithms, Tabu search, simulated annealing, and ant colony optimisation. This paper presents a new ant colony optimisation algorithm for solving multiple objective optimisation problems, and it is named as the random weight-based ant colony optimisation algorithm (RWACO). The proposed algorithm is applied to the bi-objective quadratic assignment problem and evaluates the performance by comparing with some recently developed multi-objective ant colony optimisation algorithms. The experimental results have shown that the proposed algorithm performs better than the other multi-objective ACO algorithms considered in this study.
How to Cite: Ariyasingha, I.D.I.D. and Fernando, T.G.I., 2019. A New Multi-Objective Ant Colony Optimisation Algorithm for Solving the Quadratic Assignment Problem. Vidyodaya Journal of Science, 22(1), pp.1–11. DOI: http://doi.org/10.4038/vjs.v22i1.6060
Published on 14 Nov 2019.
Peer Reviewed

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