A hybrid algorithm for identifying global and local minima when optimizing functions with many minima

Salhi, Saı̈d; Queen, N.M.

doi:10.1016/s0377-2217(02)00871-8

Cited by 39 publications

(26 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other methods that one may consider, can be traced in [13,14] for Simulated Annealing and in [15,16] for Tabu Search. Simulated Annealing aims in discovering one global minimum only.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

MinFinder: Locating all the local minima of a function

Tsoulos

Lagaris

2006

Computer Physics Communications

View full text Add to dashboard Cite

A new stochastic clustering algorithm is introduced that aims to locate all the local minima of a multidimensional continuous and differentiable function inside a bounded domain. The accompanying software (MinFinder) is written in ANSI C++. However, the user may code his objective function either in C++, C or Fortran 77. We compare the performance of this new method to the performance of Multistart and Topographical Multilevel Single Linkage Clustering on a set of benchmark problems. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be trapped in any local minimum. Global optimization is then the appropriate tool. For example, solving a non-linear system of equations via optimization, employing a "least squares" type of objective, one may encounter many local minima that do not correspond to solutions, i.e. they are far from zero. Method of solution: Using a uniform pdf, points are sampled from the rectangular search domain. A clustering technique, based on a typical distance and a gradient criterion, is used to decide from which points a local search should be started. The employed local procedure is a BFGS version due to Powell. Further searching is terminated when all the local minima inside the search domain are thought to be found. This is accomplished via the double-box rule. Program summary

show abstract

“…Other methods that one may consider, can be traced in [13,14] for Simulated Annealing and in [15,16] for Tabu Search. Simulated Annealing aims in discovering one global minimum only.…”

Section: Resultsmentioning

confidence: 99%

“…Simulated Annealing aims in discovering one global minimum only. The hybrid method [16] that combines Simulated Annealing, Tabu Search and a descent method is designed to discover all the global minima and some "important" local minima as well. We have used the Double Box stopping rule in all methods.…”

Section: Resultsmentioning

confidence: 99%

MinFinder: Locating all the local minima of a function

Tsoulos

Lagaris

2006

Computer Physics Communications

View full text Add to dashboard Cite

show abstract

“…Both globalglobal and global-local hybrids have been reported so far. The former are typically used to compensate for intrinsic weak points of evolutionary algorithms that come from their natural analogues (Robinson et al, 2002;Salhi & Queen, 2004;Paszkowicz, 2006;Grimaccia et al, 2007); whereas the latter are used for boosting the algorithm efficiency at the later stage of optimization and/or learning purposes (Chelouah & Siarry, 2003;Ishibuchi, 2003;Haupt & Chung, 2003;Elmihoub et al, 2006;Paszkowicz, 2006;Ngo et al, 2007;QuevedoTeruel et al, 2007;Boriskin & Sauleau, 2011a).…”

Section: Hybridization Of Different Optimization Techniquesmentioning

confidence: 99%

Hybrid genetic algorithm for fast electromagnetic synthesis

Boriskin

Sauleau

2010

2010 International Kharkov Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves

View full text Add to dashboard Cite

“…Additional contributions may be found in [6], [18], [20], [21]. Stretched Simulated Annealing (SSA) was also proposed [13], combining simulated annealing and a stretching function technique, to solve unconstrained multilocal programming problems.…”

Section: Introductionmentioning

confidence: 99%

coPSSA - Constrained Parallel Stretched Simulated Annealing

Rufino

Pereira

Pidanič

2015

2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA)

View full text Add to dashboard Cite

Abstract-Parallel Stretched Simulated Annealing (PSSA) solves unconstrained multilocal programming optimization problems in distributed memory clusters, by applying the Stretched Simulated Annealing optimization method, in parallel, to multiple sub-domains of the original feasible region. This work presents coPSSA (constrained Parallel Stretched Simulated Annealing), an hybrid application that combines shared memory based parallelism with PSSA, in order to efficiently solve constrained multilocal programming problems. We devise and evaluate two different parallel strategies for the search of solutions to these problems. Evaluation results from a small set of test problems often reach superlinear speedup in the solution search time, thus proving the merit of the coPSSA parallelization approach.

show abstract

A hybrid algorithm for identifying global and local minima when optimizing functions with many minima

Cited by 39 publications

References 18 publications

MinFinder: Locating all the local minima of a function

MinFinder: Locating all the local minima of a function

Hybrid genetic algorithm for fast electromagnetic synthesis

coPSSA - Constrained Parallel Stretched Simulated Annealing

Contact Info

Product

Resources

About