Simulated Annealing for Multi Objective Optimization Problems

Serafini, Paolo

doi:10.1007/978-1-4612-2666-6_29

Cited by 120 publications

(81 citation statements)

References 5 publications

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“…In order to guarantee that a dispersed set of efficient solutions is obtained, several techniques have been proposed; these can be classified into one of two main groups: Methods that aggregate the objective functions and dynamically modify the search direction during the search process or within several runs [3,6,7,9,10,19,23], and methods that are based on a Paretolike dominance relation as an acceptance criterion in the search to distinguish between candidate solutions [24,13]; this second type of methods avoid the aggregation of objectives. Recently, empirical evidence was gathered suggesting that methods belonging to the first group perform particularly well [10], the main reason being that local search algorithms can deal more easily with aggregated objective functions.…”

Section: Introductionmentioning

confidence: 99%

A Two-Phase Local Search for the Biobjective Traveling Salesman Problem

Paquete

Stützle

2003

Lecture Notes in Computer Science

100

103

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Abstract. This article proposes the Two-Phase Local Search for finding a good approximate set of non-dominated solutions. The two phases of this procedure are to (i) generate an initial solution by optimizing only one single objective, and then (ii) to start from this solution a search for non-dominated solutions exploiting a sequence of different formulations of the problem based on aggregations of the objectives. This second phase is a single chain, using the local optimum obtained in the previous formulation as a starting solution to solve the next formulation. Based on this basic idea, we propose some further improvements and report computational results on several instances of the biobjective TSP that show competitive results with state-of-the-art algorithms for this problem.

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Section: Introductionmentioning

confidence: 99%

A Two-Phase Local Search for the Biobjective Traveling Salesman Problem

Paquete

Stützle

2003

Lecture Notes in Computer Science

100

103

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“…For instance, [24] has extended simulating annealing algorithm, [13] did the same for tabu search algorithm and [2,22] extended the steepest descent algorithm to its multi-objective version. Moreover, one field that has been very active during the last two decades is the evolutionary multi-objective optimisation (EMO).…”

Section: Multi-objective Local Searchmentioning

confidence: 99%

Solving a Novel Multi-Objective Inventory Location Problem by means of a Local Search Algorithm

Lagos¹,

Moreno-Vega²,

Guerrero³

et al. 2016

SIC

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Problems in operations research are usually modelled as single objective ones even though there exist several goals that should be attained. Multiple reasons of why inherently multi-objective problems are modelled as single objective can be identified: better understanding of the problem features, simplification of the mathematical formulation of the problem, among others. However, summarising (usually conflicting) objectives into one objective function can be a sign of over-simplification in the problem modelling process. In this paper we extend a single objective inventory location problem formulation to a multi-objective one. We consider two conflicting objectives, namely, location cost and inventory cost. As a result, we obtain a complex multi-objective non-linear integer programming problem. Like its single objective formulation, this multi-objective problem cannot be solved by exact methods as the number of decision variables increases. Thus, a simple yet effective multi-objective local search algorithm is implemented in this paper.

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“…They usually use several runs of single-objective SA algorithms, and mainly differ by the acceptance rules of new solutions. The first SA for multi-objective algorithm, proposed by Serafini [59], uses the following acceptance criterion. If the new solution dominates the current one, this new solution is accepted to replace the current one.…”

Section: Scalarization-based Multi-objective Optimizationmentioning

confidence: 99%

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

Dubois-Lacoste

López-Ibáñez

Stützle

2013

Hybrid Metaheuristics

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In this chapter, we review metaheuristics for solving multi-objective combinatorial optimization problems, when no information about the decision maker's preferences is available, that is, when problems are tackled in the sense of Pareto optimization. Most of these metaheuristics follow one of the two main paradigms to tackle such problems using metaheuristics. The first paradigm is to rely on Pareto dominance when exploring the search space. The second paradigm is to tackle several single-objective problems to find several solutions that are non-dominated for the original problem; in this case, one may exploit existing, efficient single-objective algorithms, but the performance depends on the definition of the set of scalarized problems. There are also a number of approaches in the literature that combine both paradigms. However, this is usually done in a relatively ad-hoc way. In this paper, we review two conceptually simple methods representative of each paradigm: Pareto local search and Two-phase Local Search. The hybridization of these two strategies provides a general framework for engineering stochastic local search algorithms that can be used to improve over the state-of-the-art for several, widely studied problems.

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Simulated Annealing for Multi Objective Optimization Problems

Cited by 120 publications

References 5 publications

A Two-Phase Local Search for the Biobjective Traveling Salesman Problem

A Two-Phase Local Search for the Biobjective Traveling Salesman Problem

Solving a Novel Multi-Objective Inventory Location Problem by means of a Local Search Algorithm

Combining Two Search Paradigms for Multi-objective Optimization: Two-Phase and Pareto Local Search

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