Modified differential evolution approach for optimization of planar parallel manipulators force capabilities

Weihmann, Lucas; Martins, Daniel; Coelho, Leandro dos Santos

doi:10.1016/j.eswa.2011.11.099

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…In several cases, the original DE algorithm was improved with additional algorithmic components exploiting certain aspects of a given class of problems. In [45] a modified DE approach is proposed to improve the search performance by using generation-varying control parameters to prevent premature convergence to local minima. A hybrid algorithm using DE as an evolutionary framework and a crossover-based local search was proposed in [25,26].…”

Section: Related Workmentioning

confidence: 99%

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

Teijeiro

Pardo

Penas³

et al. 2017

Cluster Comput

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Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloudbased solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation).

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Section: Related Workmentioning

confidence: 99%

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

Teijeiro

Pardo

Penas³

et al. 2017

Cluster Comput

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“…In several cases, the original DE algorithm was improved with additional algorithmic components exploiting certain aspects of a given class of problems. In [20] a modified DE approach using generation-varying control parameters is proposed to improve the search performance in preventing of premature convergence to local minima. A hybrid algorithm using DE as an evolutionary framework and a crossover-based local search was proposed in [21,22].…”

Section: Related Workmentioning

confidence: 99%

Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

Penas

Banga

González

et al. 2015

Applied Soft Computing

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“…There are several methodologies which allow us to obtain a complete static analysis of robotic mechanisms; however, in this paper the formalism presented by Davies (Davies, 1983c) is used as the primary mathematical tool to analyze the mechanisms statically. The Davies method appears in many publications in the literature and further explanations regarding its use can be found in (Davies, 1983a,b,c;Cazangi, 2008;Weihmann et al, 2011Weihmann et al, , 2012Mejia et al, 2015).…”

Section: Statics Of Robotic Mechanismsmentioning

confidence: 99%

“…DE is a very simple population based, stochastic function minimizer and very powerful at the same time. This algorithm is commonly accepted as one of the most successful algorithms for the global continuous optimization problem (Weihmann et al, 2012).…”

Section: Diferential Evolution (De) Algorithmmentioning

confidence: 99%

“…DE optimizes a problem by maintaining a population of candidate solutions, and creating new candidate solutions by combining existing ones, according to its simple formula, and then keeping whichever candidate solution has the best score or fitness on the optimization problem at hand. In this way, the optimization problem is treated as a black box that merely provides a measure of quality given a candidate solution, and therefore, the gradient is not needed (Weihmann et al, 2012).…”

Section: Diferential Evolution (De) Algorithmmentioning

confidence: 99%

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Wrench capability polytopes in redundant parallel manipulators

Mejía¹,

Frantz²,

Simas³

et al. 2015

23rd ABCM International Congress of Mechanical Engineering

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In this paper the characteristic wrench capability polytope of three different planar parallel manipulators is obtained from the optimization of its static equations. In order to solve the problem regarding the global optimization, an evolutionary algorithm known as differential evolution (DE) is used. The objective function of the optimization problem of force capability is defined by employing the screw theory and Davies method as a primary mathematical tool. Finally, some force capability polytopes are obtained for each studied manipulator.

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Modified differential evolution approach for optimization of planar parallel manipulators force capabilities

Cited by 16 publications

References 35 publications

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

Wrench capability polytopes in redundant parallel manipulators

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