Simulated annealing

Aarts, Ehl Emile; Korst, Jan; Laarhoven, Peter J. M. van

doi:10.2307/j.ctv346t9c.9

Cited by 90 publications

(105 citation statements)

References 0 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…Simulated annealing takes inspiration from the annealing process in crystals, which assume a low energy configuration when cooled with an appropriate cooling schedule [32,33,34,35,36]. The principal idea is the exploration of the search space via a local search procedure.…”

Section: Literature Overviewmentioning

confidence: 99%

“…At every iteration k, the temperature is decreased according to the formula T k = αT k−1 , where the parameter α, usually called cooling rate, is such that 0 < α < 1. If after n · q · r iterations the quality of the best solution is not improved, the process known as re-heating [32] is applied: the temperature is increased by adding T 0 to the current temperature. Besides the local search procedure used, the difference between the EXACT-cost, the VRPSD-cost and the TSP-cost implementations consists in the way Cost(s ) and Cost(s) in Equation 8 are computed.…”

Section: Metaheuristicsmentioning

confidence: 99%

See 1 more Smart Citation

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Pellegrini

Birattari

2011

Intelligent Computational Optimization in Engineering

View full text Add to dashboard Cite

Abstract. Metaheuristics are a class of effective algorithms for optimization problems. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. According to the amount of development effort, we say that an implementation of a metaheuristic is either an out-of-the-box version or a custom one. The possibility of implementing metaheuristics in such a flexible way is one of the major strengths of these algorithms. Nonetheless, it also hides some possible catches. In particular, it should be noticed that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. The goal of this analysis is to stress that these two ways of using metaheuristics are different. As a case study, we focus on the vehicle routing problem with stochastic demand and on five among the most successful metaheuristics-namely, tabu search, simulated annealing, genetic algorithms, iterated local search, and ant colony optimization. We show that the relative performance of these algorithms strongly varies whether one considers out-of-the-box implementations or custom ones, in which the parameters are accurately fine-tuned. Moreover, we underline the relevance of clearly stating the framework in which the results reported in the literature have been obtained. To this aim, we consider also an implementation of the same algorithms as described in the literature.

show abstract

Section: Literature Overviewmentioning

confidence: 99%

Section: Metaheuristicsmentioning

confidence: 99%

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Pellegrini

Birattari

2011

Intelligent Computational Optimization in Engineering

View full text Add to dashboard Cite

show abstract

“…As we were able to produce superior solutions to the published fixtures we have now decided to utilise more sophisticated methods, due to the large execution times of DFS which were typically a few hours for each division. In this work we utilise CPLEX as a replacement for DFS and simulated annealing [1] as a replacement for the local search. This reduces the overall execution time from tens of hours to a few minutes.…”

Section: Methodsmentioning

confidence: 99%

Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives

Kendall

McCollum

Cruz³

et al. 2013

Hybrid Metaheuristics

View full text Add to dashboard Cite

In previous work the distance travelled by UK football clubs, and their supporters, over the Christmas/New Year period was minimised. This is important as it is not only a holiday season but, often, there is bad weather at this time of the year. Whilst searching for good quality solutions for this problem, various constraints have to be respected. One of these relates to clashes, which measures how many paired teams play at home on the same day. Whilst the supporters have an interest in minimising the distance they travel, the police also have an interest in having as few pair clashes as possible. This is due to the fact that these fixtures are more expensive, and difficult, to police. However, these two objectives (minimise distance and minimise pair clashes) conflict with one another in that a decrease in one intuitively leads to an increase in the other. This paper explores this question and shows that there are compromise solutions which allow fewer pair clashes but does not statistically increase the distance travelled. This paper provides a more comprehensive study of the initial results presented at the previous PATAT conference. We present a more detailed set of computational experiments, along with a greater number of datasets. We conclude that it is sometimes possible to reduce the number of pair clashes whilst not significantly increasing the overall distance that is travelled.

show abstract

“…Theoretical results on non-homogeneous Markov chains (Aarts and Korst, 1988;Aarts et al, 2005) state that under particular conditions on the cooling schedule, the algorithm converges in probability to a global minimum as k → +∞. More precisely, calling p k the probability to find a global minimum after k steps, then there exists Γ ∈ such that +∞ k=1 exp Γ T k → +∞ if and only if lim k→∞ p k = 1.…”

Section: Single-solution Metaheuristicsmentioning

confidence: 99%

The development and application of metaheuristics for problems in graph theory: a computational study

Consoli¹

2011

4OR-Q J Oper Res

View full text Add to dashboard Cite

Simulated annealing

Cited by 90 publications

References 0 publications

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Out-of-the-Box and Custom Implementation of Metaheuristics. A Case Study: The Vehicle Routing Problem with Stochastic Demand

Scheduling English Football Fixtures: Consideration of Two Conflicting Objectives

The development and application of metaheuristics for problems in graph theory: a computational study

Contact Info

Product

Resources

About