Ant colony algorithms for the travelling salesman problem and the quadratic assignment problem

2018 ◽  
pp. 409-442 ◽  
Author(s):  
Nikola Ivkovic
Keyword(s):  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Travelling Salesman ◽  
Ant Colony Algorithms

scholarly journals EFFICIENT METAHEURISTIC ALGORITHMS FOR THE MULTI-STRIPE TRAVELLING SALESMAN PROBLEM

2020 ◽  
Vol 36 (3) ◽  
pp. 233-250
Author(s):  
Ban Ha Bang
Keyword(s):  
Quadratic Assignment Problem ◽  
Travelling Salesman Problem ◽  
Computation Time ◽  
Metaheuristic Algorithms ◽  
Neighborhood Search ◽  
Quadratic Assignment ◽  
Travelling Salesman ◽  
Local Optima ◽  
Benchmark Instances ◽  
Main Components

The Multi-stripe Travelling Salesman Problem (Ms-TSP) is an extension of the Travelling Salesman Problem (TSP). In the \textit{q}-stripe TSP with $q \geq 1$, the objective function sums the costs for travelling from one customer to each of the next \textit{q} customers along the tour. The resulting \textit{q}-stripe TSP generalizes the TSP and forms a special case of the Quadratic Assignment Problem. To solve medium and large size instances, a metaheuristic algorithm is proposed. The proposed algorithm has two main components, which are construction and improvement phases. The construction phase generates a solution using Greedy Randomized Adaptive Search Procedure (GRASP) while the optimization phase improves the solution with several variants of Variable Neighborhood Search, both coupled with a technique called Shaking Technique to escape from local optima. In addition, Adaptive Memory is integrated into our algorithms to balance between the diversification and intensification. To show the efficiency of our proposed metaheuristic algorithms, we extensively experiment on benchmark instances. The results indicate that the developed algorithms can produce efficient and effective solutions at a reasonable computation time.

scholarly journals A Hybrid Ant Colony Algorithm for Quadratic Assignment Problem

2013 ◽  
Vol 7 (1) ◽  
pp. 51-54 ◽  
Author(s):  
Guo Hong
Keyword(s):  
Ant Colony Optimization ◽  
Assignment Problem ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Optimal Solutions ◽  
Combinatorial Optimization Problems

Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization problems. Ant colony optimization (ACO) is one of new bionic optimization algorithms and it has some characteristics such as parallel, positive feedback and better performances. ACO has achieved in solving quadratic assignment problems. However, its solution quality and its computation performance need be improved for a large scale QAP. In this paper, a hybrid ant colony optimization (HACO) has been proposed based on ACO and particle swarm optimization (PSO) for a large scale QAP. PSO algorithm is combined with ACO algorithm to improve the quality of optimal solutions. Simulation experiments on QAP standard test data show that optimal solutions of HACO are better than those of ACO for QAP.

scholarly journals ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rafid Sagban ◽  
Ku Ruhana Ku-Mahamud ◽  
Muhamad Shahbani Abu Bakar
Keyword(s):  
Ant Colony Optimization ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Optimization Algorithms

A statistical machine learning indicator,ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

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