Multiobjective Simulated Annealing: Principles and Algorithm Variants

Simulated annealing is a stochastic local search method, initially introduced for global combinatorial mono-objective optimisation problems, allowing gradual convergence to a near-optimal solution. An extended version for multiobjective optimisation has been introduced to allow a construction of nea...

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Bibliographic Details
Published in:Advances in Operations Research Vol. 2019; no. 2019; pp. 1 - 13
Main Author: Amine, Khalil
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
John Wiley & Sons, Inc
Subjects:
Algorithms
Annealing
Archives & records
Combinatorial analysis
Computer simulation
Cooling
Design optimization
Engineering
Equilibrium
Genetic algorithms
High temperature
Multiple objective analysis
Neighborhoods
Operations management
Operations research
Pareto optimization
Pareto optimum
Simulated annealing
State-of-the-art reviews
Germany
ISSN:1687-9147, 1687-9155
Online Access:Get full text
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Summary:Simulated annealing is a stochastic local search method, initially introduced for global combinatorial mono-objective optimisation problems, allowing gradual convergence to a near-optimal solution. An extended version for multiobjective optimisation has been introduced to allow a construction of near-Pareto optimal solutions by means of an archive that catches nondominated solutions while exploring the feasible domain. Although simulated annealing provides a balance between the exploration and the exploitation, multiobjective optimisation problems require a special design to achieve this balance due to many factors including the number of objective functions. Accordingly, many variants of multiobjective simulated annealing have been introduced in the literature. This paper reviews the state of the art of simulated annealing algorithm with a focus upon multiobjective optimisation field.
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ISSN:1687-9147
1687-9155
DOI:10.1155/2019/8134674