Topics in global optimization

Levy, A. V.; Montalvo, Antonio; Gómez, Susana; Calderón, Alma Graciela García

doi:10.1007/bfb0092957

Cited by 43 publications

(17 citation statements)

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“…and Timmer [1983] for further references), deterministic methods such as the tunneling algorithm of Levy et al [1981], and a large class of methods for constrained global optimization problems with concave objective functions (see e.g. Rosen [1983]).…”

Section: Discussionmentioning

confidence: 99%

Parallel Computing in Optimization

Migdalas

Pardalos²,

Story³

1997

Applied Optimization

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Section: Discussionmentioning

confidence: 99%

Parallel Computing in Optimization

Migdalas

Pardalos²,

Story³

1997

Applied Optimization

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“…The benchmark functions [15] on which the proposed algorithm has been tested and compared to other methods in the literature, and the corresponding parameters are listed in Table 1. To eliminate the influence of different initial swarms, we implement 200 experiments for each test and the maximum number of PSO iterations is set to be 500, swarm size is 60 for 30-dimension functions and 30 for others.…”

Section: The Proposed Algorithm and Experimental Designmentioning

confidence: 99%

Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm

Wang

Qiu

2005

Machine Learning: ECML 2005

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Abstract.A new hybrid Particle Swarm Optimization (PSO) algorithm is proposed in this paper based on the Nonlinear Simplex Search (NSS) method for multimodal function optimizing tasks. At late stage of PSO process, when the most promising regions of solutions are fixed, the algorithm isolates particles that fly very close to the extrema and applies the NSS method to them to enhance local exploitation searching. Explicit experimental results on famous benchmark functions indicate that this approach is reliable and efficient, especially on multimodal function optimizations. It yields better solution qualities and success rates compared to other three published methods.

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“…the point of the trajectory with the smallest function value, is also included into the maintained population P . The two available points x 1 0 and x 1 m are then combined, according to (4), to give the starting point x 2 0 of the second trajectory. The SF operator is employed at this initial stage because the DE mutation and recombination operators can be applied only on populations of size popsize 4.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

Evolutionary Operators in Global Optimization with Dynamic Search Trajectories

Laskari

Parsopoulos

Vrahatis

2003

Numerical Algorithms

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One of the most commonly encountered approaches for the solution of unconstrained global optimization problems is the application of multi-start algorithms. These algorithms usually combine already computed minimizers and previously selected initial points, to generate new starting points, at which, local search methods are applied to detect new minimizers. Multi-start algorithms are usually terminated once a stochastic criterion is satisfied. In this paper, the operators of the Differential Evolution algorithm are employed to generate the starting points of a global optimization method with dynamic search trajectories. Results for various well-known and widely used test functions are reported, supporting the claim that the proposed approach improves drastically the performance of the algorithm, in terms of the total number of function evaluations required to reach a global minimizer.

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Topics in global optimization

Cited by 43 publications

References 0 publications

Parallel Computing in Optimization

Parallel Computing in Optimization

Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm

Evolutionary Operators in Global Optimization with Dynamic Search Trajectories

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