Comparison Between a Concurrent and a Sequential Optimization Methodology for Serial Manipulators Using Metaheuristics

Valdez, S. Ivvan; Botello-Aceves, Salvador; Becerra, Héctor M.

doi:10.1109/tii.2018.2795103

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…Sequential optimization is firstly proposed by Platt for training support vector machines (SVMs) [31], [32]. Sequential optimization breaks the large QP problem into a series of smallest possible QP problems.…”

Section: Sequential Optimizationmentioning

confidence: 99%

Online Semi-Supervised Learning With Multiple Regularization Terms

Chen

Sun

et al. 2019

IEEE Access

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Online semi-supervised learning (OS 2 L) has received much attention recently because of its well practical usefulness. Most of the existing studies of OS 2 L are related to manifold regularization. In this paper, we introduce a novel OS 2 L framework with multiple regularization terms based on the notion of ascending the dual function in constrained optimization. Using the Fenchel conjugate, different semisupervised regularization terms can be integrated into the dual function easily and directly. This approach is derived by updating limited dual coefficient variables on each learning round. To be practical, we also employ buffering strategies and sparse approximation approaches in this paper. The experimental studies show that our methods achieve accuracy comparable to offline algorithms while consuming less time and memory. Especially, our OS 2 L algorithms can handle the settings where the target hyperplane of classification continually drifts with the sequence of arriving instances. This paper paves a way to design and analyze OS 2 L algorithms with multiple regularization terms. INDEX TERMSOnline semi-supervised learning (OS 2 L), SVM, manifold regularization, co-regularization, Fenchel conjugate.

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Section: Sequential Optimizationmentioning

confidence: 99%

Online Semi-Supervised Learning With Multiple Regularization Terms

Chen

Sun

et al. 2019

IEEE Access

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“…Working methods vary under a system's complexity and the number of objectives to optimize. According to Botello et al [8,9], the optimization problems of robotic manipulators can be classified into three categories: The first is the static design problem, in which a homogeneous transformation or zero-order differential equation needs to be solved. Typical problems of this group are dexterity, stiffness, workspace volume, and manipulability maximization.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Ríos

Valdez

2021

Mathematics

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This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

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A Hybrid EDA/Nelder-Mead for Concurrent Robot Optimization

Valdez

Keshtkar

2019

Hybrid Intelligent Systems

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Comparison Between a Concurrent and a Sequential Optimization Methodology for Serial Manipulators Using Metaheuristics

Cited by 9 publications

References 21 publications

Online Semi-Supervised Learning With Multiple Regularization Terms

Online Semi-Supervised Learning With Multiple Regularization Terms

A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

A Hybrid EDA/Nelder-Mead for Concurrent Robot Optimization

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