Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP

Guntsch, Michael; Middendorf, Martin

doi:10.1007/3-540-45365-2_22

Cited by 130 publications

(93 citation statements)

References 9 publications

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“…Popular examples are: the diversity of solutions [50,51,52,39], stability [53,41], reactivity [41], robustness [44], cross-entropy [54], peak cover [17] and λ-branching [55] 2 . Other performance measurements were proposed exclusively to evaluate the ability of the algorithms to track and locate feasible regions such as: feasible time ratio, optimal region tracking measure, local search cover, number of evaluations for constraints.…”

Section: Measurementsmentioning

confidence: 99%

“…Other strategies aim to simply increase the diversity and maintain the knowledge gained, simultaneously, when a change occurs. For example, Guntsch and Middendorf [54,111] proposed partial restart strategies using local information, e.g., the η-strategy and τ-strategy, which take into account where the dynamic change actually occurs, e.g., which cities are added/removed for DTSP. The aim of both strategies is to give a higher degree of re-initialization to the pheromone trails closer to the offended areas.…”

Section: Increasing Diversity After a Changementioning

confidence: 99%

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A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Measurementsmentioning

confidence: 99%

Section: Increasing Diversity After a Changementioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

472

213

View full text Add to dashboard Cite

show abstract

“…Since then, several variations of DTSPs were introduced, where the set of nodes N [1,9,10,12,13,25] and/or the cost from the set of arcs A [6,17,19,21,25] cause the weight matrix W(t) to change during the optimization process. However, there is still no any unified benchmark problem for DTSPs, which makes the comparison with algorithms from the literature a very challenging task.…”

Section: Dtsp Benchmark Generatorsmentioning

confidence: 99%

“…However, there is still no any unified benchmark problem for DTSPs, which makes the comparison with algorithms from the literature a very challenging task. One popular benchmark is the DTSP where cities are exchanged: half of the cities from the problem instance are removed to create a spare pool [9,10,14], and the cities from the spare pool are then used to replace cities from the problem instance. Another popular benchmark is the DTSP where the weights of arcs change probabilistically [25,17] (the complete benchmark generator description is given in Section 2.3 since it is the benchmark generator we consider in the experiments).…”

Section: Dtsp Benchmark Generatorsmentioning

confidence: 99%

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Pre-scheduled Colony Size Variation in Dynamic Environments

Mavrovouniotis

Ιωάννου

Yang

2017

Applications of Evolutionary Computation

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Abstract. The performance of the MAX -MIN ant system (MMAS) in dynamic optimization problems (DOPs) is sensitive to the colony size. In particular, a large colony size may waste computational resources whereas a small colony size may restrict the searching capabilities of the algorithm. There is a trade off in the behaviour of the algorithm between the early and later stages of the optimization process. A smaller colony size leads to better performance on shorter runs whereas a larger colony size leads to better performance on longer runs. In this paper, pre-scheduling of varying the colony size of MMAS is investigated in dynamic environments.

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A Concise Overview of Applications of Ant Colony Optimization

Stützle

López-Ibáñez

Dorigo

2011

Wiley Encyclopedia of Operations Research and Management Science

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Ant colony optimization (ACO) [1-3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO.Despite being one of the youngest metaheuristics, the number of applications of ACO algorithms is very large. In principle, ACO can be applied to any combinatorial optimization problem for which some iterative solution construction mechanism can be conceived. Most applications of ACO deal with NP-hard combinatorial optimization problems, that is, with problems for which no polynomial time algorithms are known. ACO algorithms have also been extended to handle problems with multiple objectives, stochastic data, and dynamically changing problem information. There are extensions of the ACO metaheuristic for dealing with problems with continuous decision variables, as well.This article provides a concise overview of several noteworthy applications of ACO algorithms. This overview is necessarily incomplete because the number of currently available ACO applications goes into the hundreds. Our description of the applications

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Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP

Cited by 130 publications

References 9 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Pre-scheduled Colony Size Variation in Dynamic Environments

A Concise Overview of Applications of Ant Colony Optimization

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