Simulated Annealing

Aarts, Ehl Emile; Korst, Jan; Michiels, Wil

doi:10.1007/0-387-28356-0_7

Cited by 161 publications

(111 citation statements)

References 39 publications

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“…In this paper, we have explored the principle for simple local search. Of course, this general approach could be adapted to a wide range of heuristic and metaheuristic techniques (Aarts et al 2005). One potentially beneficial direction of future research would be to build upon a suite of different indicator-based search strategies, to develop an adaptive version of IBMOLS.…”

Section: Discussionmentioning

confidence: 98%

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The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems

Basseur

Liefooghe

et al. 2011

J Heuristics

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In the last few years, a significant number of multi-objective metaheuristics have been proposed in the literature in order to address real-world problems. Local search methods play a major role in many of these metaheuristic procedures. In this paper, we adapt a recent and popular indicator-based selection method proposed by Zitzler and Künzli in 2004, in order to define a population-based multi-objective local search. The proposed algorithm is designed in order to be easily adaptable, parameter independent and to have a high convergence rate. In order to evaluate the capacity of our algorithm to reach these goals, a large part of the paper is dedicated to experiments. Three combinatorial optimisation problems are tested: a flow shop problem, a ring star problem and a nurse scheduling problem. The experiments show that our algorithm can be applied with success to different types of multi-objective optimisation problems and that it outperforms some classical metaheuristics. Furthermore, the parameter sensitivity analysis enables us to provide some useful guidelines about how to set the parameters

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Section: Discussionmentioning

confidence: 98%

“…The recombination operator applied is the two-point crossover used in Basseur et al (2002). • RM: Apply random mutations on N randomly selected and different solutions of PO, such as in a basic simulated annealing algorithm (Aarts et al 2005). Each solution can be selected once.…”

Section: Population Generation Methodsmentioning

confidence: 99%

The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems

Basseur

Liefooghe

et al. 2011

J Heuristics

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“…Simulated annealing (for a general introduction, see Aarts, Korst, & Michiels, 2005) is a local search technique that is often used for complex combinatorial optimization problems (Brusco, 2001;Trejos & Castillo, 2000;Wilderjans, Ceulemans, & Van Mechelen, 2008). Making use of the pseudo code in Algorithm 1 and the associated notation in Table 3, we first recapitulate the general principle of the SA algorithm and subsequently discuss the specific implementation for fitting CLASSI-N solutions.…”

Section: Algorithmmentioning

confidence: 99%

“…Third, the initial T current was chosen so as to result in an average acceptance probability of 0.80 (Aarts et al, 2005). Fourth, on the basis of some pilot studies, the cooling factor α was set to 0.90 (typical values of α lie between 0.8 and 0.99, Aarts et al, 2005;Trejos & Castillo, 2000).…”

Section: Algorithmmentioning

confidence: 99%

The CLASSI-N Method for the Study of Sequential Processes

et al. 2011

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“…For a more detailed description on the practical implementation of SA refer the reader to Aarts et al [15].…”

Section: Simulated Annealing Based Adaptive Large Neighborhood Searchmentioning

confidence: 99%

An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem

Lusby

Schwierz²,

Range

et al. 2016

Artificial Intelligence in Medicine

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Objective: The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting patient comfort and treatment efficiency are maximized.Consideration must be given to uncertainty in the length of stays of patients as well as the possibility of emergency patients. Method:We develop an adaptive large neighborhood search (ALNS) procedure to solve the problem. This procedure utilizes a Simulated Annealing framework. Results: We thoroughly test the performance of the proposed ALNS approach on a set of 450 publicly available problem instances. A comparison with the current state-of-the-art indicates that the proposed methodology provides solutions that are of comparable quality for small and medium sized instances (up to 1000 patients); the two approaches provide solutions that differ in quality by approximately 1% on average. The ALNS procedure does, however, provide solutions in a much shorter time frame. On larger instances (between 1000-4000 patients) the improvement in solution quality by the ALNS procedure is substantial, approximately 3-14% on average, and as much as 22% on a single instance. The time taken to find such results is, however, in the worst case, a factor 12 longer on average than the time limit which is granted to the current state-of-the-art. Conclusion:The proposed ALNS procedure is an efficient and flexible method for solving the DPAS problem.

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Simulated Annealing

Cited by 161 publications

References 39 publications

The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems

The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems

The CLASSI-N Method for the Study of Sequential Processes

An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem

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