e-journal

Optimization Algorithms for Kinematically Optimal Design of Parallel Manipulators

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Optimal design is an inevitable step for parallel manipulators. The formulated optimal design problems are generally constrained, nonlinear, multimodal, and even without closed-form analytical expressions. Numerical optimization algorithms are thus applied to solve the problems. However, the optimization algorithms are usually chosen ad arbitrium. This paper aims to provide a guideline to choose algorithms for optimal design problems. Typical algorithms, the sequential quadratic programming (SQP) with multiple initial points, the controlled random search (CRS), the genetic algorithm (GA), the differential evolution (DE), and the particle swarm optimization (PSO), are investigated in detail for their convergence performances by using two canonical design examples, the Delta robot and the Gough–Stewart platform. It is shown that SQP with multiple initial points can be efficient for simple design problems, while DE and PSO perform effectively and steadily for all design problems. CRS can be used to generate
good initial points since it exhibits excellent convergence evolution in the starting period. Note to Practitioners—Numerical optimization algorithms are generally inevitable in solving optimal design problems of parallel manipulators. Various algorithms have been applied in literature and in engineering. This paper provides a thorough comparison on convergence performance of typical optimization algorithms, SQP with multiple initial points, CRS, GA, DE, and PSO. Two parallel manipulators, the Delta robot and the Gough–Stewart platform, are used as design examples by maximizing the effective regular workspace. Computation shows that DE and PSO are good choices for complicated optimal design problems, while SQP with multiple initial points is superior for simple problems. CRS performs excellently in the starting period. It can be used to generate good initial points.
Index Terms—Controlled random search (CRS), differential evolution (DE), genetic algorithm (GA), optimal design, optimization algorithms, parallel manipulators, particle swarm optimization (PSO), sequential quadratic programming (SQP).

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 2, APRIL 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 2

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

yuli/agus

Versi lain/terkait

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