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Multiobjective Simulated Annealing: Principles and Algorithm Variants

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  • Khalil Amine

Abstract

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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  • Khalil Amine, 2019. "Multiobjective Simulated Annealing: Principles and Algorithm Variants," Advances in Operations Research, Hindawi, vol. 2019, pages 1-13, May.
    • Handle: RePEc:hin:jnlaor:8134674
    DOI: 10.1155/2019/8134674
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    Cited by:

    1. Asma Khalil Alkhamis & Manar Hosny, 2023. "A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza," Sustainability, MDPI, vol. 15(21), pages 1-37, October.
    2. Anuraag Bukkuri & Joel S. Brown, 2021. "Evolutionary Game Theory: Darwinian Dynamics and the G Function Approach," Games, MDPI, vol. 12(4), pages 1-19, September.
    3. Maria Rossana D. de Veluz & Anak Agung Ngurah Perwira Redi & Renato R. Maaliw & Satria Fadil Persada & Yogi Tri Prasetyo & Michael Nayat Young, 2023. "Scenario-Based Multi-Objective Location-Routing Model for Pre-Disaster Planning: A Philippine Case Study," Sustainability, MDPI, vol. 15(6), pages 1-33, March.
    4. Máximo Méndez & Mariano Frutos & Fabio Miguel & Ricardo Aguasca-Colomo, 2020. "TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    5. Amarnath Bose, 2020. "Using genetic algorithm to improve consistency and retain authenticity in the analytic hierarchy process," OPSEARCH, Springer;Operational Research Society of India, vol. 57(4), pages 1070-1092, December.
    6. Ahern, Zeke & Paz, Alexander & Corry, Paul, 2022. "Approximate multi-objective optimization for integrated bus route design and service frequency setting," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 1-25.

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