A Population Based Approach for ACO

Guntsch, Michael; Middendorf, Martin

doi:10.1007/3-540-46004-7_8

Cited by 150 publications

(96 citation statements)

References 8 publications

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“…We use an idea similar to that proposed by Guntsch and Middendorf in Population-Based ACO (PB-ACO) (Guntsch and Middendorf, 2002). In PB-ACO the pheromone table is updated based on the components of good solutions found-just like in regular ACO.…”

Section: Pheromone Representation In Aco Rmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,165

681

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Section: Pheromone Representation In Aco Rmentioning

confidence: 99%

Ant colony optimization for continuous domains

Socha

Dorigo

2008

European Journal of Operational Research

1,165

681

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“…In recent years, some variants of classic intelligent optimization methods have been developed for a promising behavior on DTSPs. Guntsch and Middendorf [8] introduced a population-based ant colony optimization algorithm to solve DTSPs. They applied and compared three strategies for modifying the pheromone information instead of the restart strategy in reaction to the change of problem instances.…”

Section: Relevant Work On the Dynamic Tspmentioning

confidence: 99%

“…In this study, we adopted the method proposed in [8] to create DTSP instances based on the data of kroA100 [17], which is created by changing the costs between some cities. That is, randomly removing half of the cities from the standard TSP data set to create a spare pool.…”

Section: Experimental Designmentioning

confidence: 99%

“…It is interesting to investigate dynamic instances when changes are not so severe that the new optimal solutions might be related to the old ones. As one of the most typical NP-hard combinatorial optimization problems, the traveling salesman problem in dynamic environments (DTSP), which is caused by deletion of some cities, the change of traffic costs between cities, or the addition of cities over time, has a practical application in production and traffic systems [6,7,8] and requires optimization algorithms to be able to track the optima in new situations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems

Liu

Wang

Yang

2009

Lecture Notes in Computer Science

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Abstract. In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.

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“…In the last few years, ACO has received increased attention by the scientific community as can be seen by the growing number of publications and different application fields [4]. Even though, there are several ACO variants that can be considered, a standard approach is next presented [11].…”

Section: Ant Colony Optimization Approachmentioning

confidence: 99%

Multiobjective Multicast Routing with Ant Colony Optimization

Pinto

Barán

IFIP International Federation for Information Processing

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Abstract. This work presents a multiobjective algorithm for multicast traffic engineering. The proposed algorithm is a new version of MultiObjective Ant Colony System (MOACS), based on Ant Colony Optimization (ACO). The proposed MOACS simultaneously optimizes the maximum link utilization, the cost of the multicast tree, the averages delay and the maximum endtoend delay. In this way, a set of optimal solutions, known as Pareto set is calculated in only one run of the algorithm, without a priori restrictions. Experimental results obtained with the proposed MOACS were compared to a recently published Multiobjective Multicast Algorithm (MMA), showing a promising performance advantage for multicast traffic engineering.

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A Population Based Approach for ACO

Cited by 150 publications

References 8 publications

Ant colony optimization for continuous domains

Ant colony optimization for continuous domains

An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems

Multiobjective Multicast Routing with Ant Colony Optimization

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