In this work we consider the problem of finding all the global maximizers of a given nonlinear optimization problem. We propose a new algorithm that combines the simulated annealing (SA) method with a function stretching technique, to generate a sequence of global maximization problems that are defined whenever a new maximizer is identified. To find the global maximizers, we apply the SA algorithm to the sequence of maximization problems. Results of numerical experiments with a set of well-known test problems show that the proposed method is effective. We also compare the performance of our algorithm with other multi-global optimizers
Many statistical methods rely on numerical optimization to estimate a model\u27s parameters. Unfortu...
In this paper several probabilistic search techniques are developed for global optimization under th...
Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorit...
In this work we consider the problem of finding all the global maximizers of a given nonlinear optim...
In this paper we consider the problem of finding all the global maximizers of a given nonlinear opti...
In this talk we consider the problem of finding all the global solutions of a nonlinear optimization...
Work partially supported by FCT grant POCTI/MAT/58957/2004.In this work we consider the problem of f...
In this paper we are concerned with global optimization, which can be defined as the problem of find...
Abstract. Global optimization involves the difficult task of the identification of global extremitie...
In the early 1980s, Kirkpatrick et al. [1] and, independently, Černý [2] introduced simulated anneal...
Particle swarm and simulated annealing optimization algorithms proved to be valid in finding a globa...
This paper presents a new simulated annealing algorithm to solve constrained multi-global optimizati...
In a multiglobal optimization problem we aim to find all the global solutions of a constrained nonli...
In a multiglobal optimization problem we aim to find all the global solutions of a constrained nonli...
Simulated annealing is a global optimization method that distinguishes between different local optim...
Many statistical methods rely on numerical optimization to estimate a model\u27s parameters. Unfortu...
In this paper several probabilistic search techniques are developed for global optimization under th...
Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorit...
In this work we consider the problem of finding all the global maximizers of a given nonlinear optim...
In this paper we consider the problem of finding all the global maximizers of a given nonlinear opti...
In this talk we consider the problem of finding all the global solutions of a nonlinear optimization...
Work partially supported by FCT grant POCTI/MAT/58957/2004.In this work we consider the problem of f...
In this paper we are concerned with global optimization, which can be defined as the problem of find...
Abstract. Global optimization involves the difficult task of the identification of global extremitie...
In the early 1980s, Kirkpatrick et al. [1] and, independently, Černý [2] introduced simulated anneal...
Particle swarm and simulated annealing optimization algorithms proved to be valid in finding a globa...
This paper presents a new simulated annealing algorithm to solve constrained multi-global optimizati...
In a multiglobal optimization problem we aim to find all the global solutions of a constrained nonli...
In a multiglobal optimization problem we aim to find all the global solutions of a constrained nonli...
Simulated annealing is a global optimization method that distinguishes between different local optim...
Many statistical methods rely on numerical optimization to estimate a model\u27s parameters. Unfortu...
In this paper several probabilistic search techniques are developed for global optimization under th...
Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorit...
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