Multiple-Try Simulated Annealing for Constrained Optimization

Diana Barro (), Roberto Casarin and Anthony Osuntuyi
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Diana Barro: Ca’ Foscari University of Venice
Roberto Casarin: Ca’ Foscari University of Venice; European Center for Living Technology
Anthony Osuntuyi: Ca’ Foscari University of Venice

No 2025: 20, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: In the large class of robust optimization methods, stochastic programming and stochastic optimization gained popularity thanks to the theoretical guarantees of the algorithms. This paper focuses on simulated annealing, a stochastic-based algorithm for numerical optimization problems with a good global exploration ability. However, the global optimum values cannot always be guaranteed without a slowly decreasing cooling schedule. This ultimately negatively impacts the convergence speed of the algorithm. This deficiency is overcome in this study by a new stochastic optimization algorithm built on generalized Metropolis and simulated annealing (SA) algorithms. The ergodicity of the proposed constrained multiple-try Metropolis SA is proved. Several constrained optimization benchmarks and challenging real-world high-dimensional problems from finance were considered for assessing the performance of the proposed algorithm.

Keywords: Simulated annealing; multiple-try Metropolis; constrained optimization; penalty method (search for similar items in EconPapers)
JEL-codes: C61 C63 G11 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2025
New Economics Papers: this item is included in nep-cmp
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