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 multiobjective 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. Keywords: ACO, multi-objective problem, QAP, travelling salesman problem
Highlights
Real ants in a colony find the shortest path between their nest and the food source using the pheromone trails lying on the ground
This paper proposes a new ant colony optimisation algorithm in the context of multiple objectives for solving the bi-objective quadratic assignment problem, and it is named as random weight-based ant colony optimisation algorithm (RWACO).The performance of the new algorithm is evaluated by comparing it with recently developed multi-objective ant colony optimisation algorithms
The proposed ant colony optimisation algorithm (RWACO Algorithm) This paper proposes a new ant colony optimisation algorithm, random weight-based ant colony optimisation (RWACO) algorithm, for solving quadratic assignment problem with multiple objective functions
Summary
Real ants in a colony find the shortest path between their nest and the food source using the pheromone trails lying on the ground. Koopmans and Beckmann (1957) introduced the scalar Quadratic Assignment Problem (QAP) to model a plant-location problem. QAP is one of the hardest combinatorial optimisation problems (Stutzle, 1998) and an NP-hard problem which can be defined as the problem of assigning a set of facilities to a set of locations. Knowles and Corne (2003) introduced multi-objective quadratic assignment problem (mQAP) by considering several flow matrices and the same distance matrix and it can be defined as follows: C(π) ={C1(π),C2(π),...,Cm(π)}. I=1 j=1 where n is the number of facilities/locations and C(π) is the vector of m objective functions. dij is the distance between location
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