Applying Population Based ACO to Dynamic Optimization Problems

Guntsch, Michael; Middendorf, Martin

doi:10.1007/3-540-45724-0_10

Cited by 140 publications

(115 citation statements)

References 5 publications

Supporting

Mentioning

112

Contrasting

Unclassified

Order By: Relevance

“…The benchmark problems that generate dynamic environments following this methodology are specified for a single problem. In some other cases, researchers prefer to create their own customized benchmark problems that aim to model some realworld scenarios [65,66,37,67,68,69] which again are developed for a specific problem, or even a specific instance of the problem.…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

“…Usually, the components that define the discrete/combinatorial optimization problem are modified and are specific to the problem. For the dynamic TSP (DTSP), when a change occurs, either nodes may be replaced/removed [66] or the weights of the arc may increase/decrease [65,21]. The dynamic changes in dynamic VRP (DVRP) may occur on the weights of the arcs [33] or new customers may be revealed [67].…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

“…There are two main ACO frameworks, i.e., evaporationbased [92,93] and population-based [66]. Their difference lies in the way pheromone is updated.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

“…One of the most popular ACO for the DTSP is the population-based ACO (P-ACO) [66]. P-ACO maintains a memory of the best ants that is used for updating the pheromones.…”

Section: Memory Schemesmentioning

confidence: 99%

See 3 more Smart Citations

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

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.

show abstract

Section: The Generation Of Dynamicsmentioning

confidence: 99%

Section: The Generation Of Dynamicsmentioning

confidence: 99%

“…There are two main ACO frameworks, i.e., evaporationbased [92,93] and population-based [66]. Their difference lies in the way pheromone is updated.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

“…One of the most popular ACO for the DTSP is the population-based ACO (P-ACO) [66]. P-ACO maintains a memory of the best ants that is used for updating the pheromones.…”

Section: Memory Schemesmentioning

confidence: 99%

See 2 more Smart Citations

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%

Pre-scheduled Colony Size Variation in Dynamic Environments

Mavrovouniotis

Ιωάννου

Yang

2017

Applications of Evolutionary Computation

View full text Add to dashboard Cite

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.

show abstract