Analysis of the Best-Worst Ant System and Its Variants on the QAP

Cordón, Óscar; Viana, Iñaki Fernández de; Herrera, Francisco

doi:10.1007/3-540-45724-0_20

Cited by 48 publications

(52 citation statements)

References 2 publications

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“…Dorigo contributed several research works in the swarm intelligence [8], simply says Dorigo may designate as the father of ACO. Ant-Q developed by Gambardella & Dorigo [9], Ant Colony System developed by Dorigo & Gambardella [10], MaxMin AS developed by Stutzle & Hoos [11], Rank-Based AS developed by Bullnheimer et al [12], ANTS proposed by Maniezzo [13], BWAS proposed by Cordon et al [14], Hyper-Cube AS proposed by Blum et al [15] are some of the successful ant optimization algorithm.…”

Section: )mentioning

confidence: 99%

“…The probabilistic selection of the paths allows searching large number of solutions. ACO has been applied successfully to discrete optimization problems such as the traveling salesman problem [13], routing [14], and [15]. A number of proofs for the convergence to the optimum path of the ACO can be found in [16] and [17].…”

Section: Aco Implementation and Performance Evaluationmentioning

confidence: 99%

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Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

B.¹,

Baskaran²

2011

IJCIS

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Ant colony optimization (ACO) takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper reviewing varies systematic approach on recent research and implementation of ACO. Finally it presents the experimental result of ACO which is applied for routing problem and compared with existing algorithms.

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Section: )mentioning

confidence: 99%

Section: Aco Implementation and Performance Evaluationmentioning

confidence: 99%

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

B.¹,

Baskaran²

2011

IJCIS

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“…We address this issue by comparing several ACO variants. In addition to ACS, we have considered the following three high performing variants: MAX -MIN ant system (MMAS) (Stützle and Hoos, 2000), rank-based ant system (RAS) (Bullnheimer et al, 1999), and best-worst ant system (BWAS) (Cordón et al, 2002). In all three variants, m ants construct solutions only using the random proportional rule and they differ from ACS with respect to the pheromone update procedure.…”

Section: Comparison Between Estimation-based Aco Variantsmentioning

confidence: 99%

“…The default parameter values for each variant are chosen reasonably close to the values proposed in the ACO literature for the TSP Bullnheimer et al, 1999;Cordón et al, 2002): in all the variants m, α, and β are set to 10, 1.0, and 2.0, respectively; in ACS-EE, ρ and q 0 are set to 0.1 and 0.98, respectively; in MMAS-EE, ρ is set to 0.2; in RAS-EE, ρ and w are set to 0.5 and 6, respectively; in BWAS-EE, ρ is set to 0.…”

Section: Experiments With Default Parameter Valuesmentioning

confidence: 99%

Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem

et al. 2009

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The use of ant colony optimization for solving stochastic optimization problems has received a significant amount of attention in recent years. In this paper, we present a study of enhanced ant colony optimization algorithms for tackling a stochastic optimization problem, the probabilistic traveling salesman problem. In particular, we propose an empirical estimation approach to evaluate the cost of the solutions constructed by the ants. Moreover, we use a recent estimation-based iterative improvement algorithm as a local search. Experimental results on a large number of problem instances show that the proposed ant colony optimization algorithms outperform the current best algorithm tailored to solve the given problem, which also happened to be an ant colony optimization algorithm. As a consequence, we have obtained a new state-of-the-art ant colony optimization algorithm for the probabilistic traveling salesman problem.

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“…ACS was developed by Dorigo and Gambardella [24] for improvement of AS performance. Different procedures have been done to improve the ACO algorithm performance in several articles; see [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

An ant colony system for solving fuzzy flow shop scheduling problem

Pour

Ashkezari²,

Andargoli³

2012

IJET

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During the past years, the flow shop has been regarded by many researchers and some extensive investigations have been done on this respect. Flow Shop includes n works performed on m machines in a same sequence. It is very difficult in the real world to determine the exact process time of an operation on a machine. Therefore, we consider in this article the process time as trapezoidal fuzzy numbers. Our purpose is that we obtain a sequence of works using such fuzzy numbers in order to minimize maximum fuzzy time of completion entire jobs or fuzzy makespan. We offered an optimization algorithm of Ant Colony System (ACS) to solve this problem. Finally, we present computational results for explanation and comparison with other articles in future.

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Analysis of the Best-Worst Ant System and Its Variants on the QAP

Cited by 48 publications

References 2 publications

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem

An ant colony system for solving fuzzy flow shop scheduling problem

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