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Book ChapterDOI

Coupler-Curve synthesis of a planar four-bar mechanism using NSGA-II

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TLDR
The proposed enhancements of the basic scheme of NSGA-II deliver promising improvements in terms of accuracy, and rate of convergence of the solutions, and are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis.
Abstract
This paper applies a genetic algorithm-based optimisation procedure, namely, NSGA-II, to the problem of synthesis of a four-bar mechanism. The internal parameters of ${\texttt{\rm NSGA-II}}$ are tuned using a Design of Experiments (DoE) procedure to enhance the quality of the final results. Constraints are handled through a penalty formulation. Further, a scaling function is introduced, which transforms the penalty terms in a manner that leads to faster convergence of the solutions. The theoretical developments are illustrated via applications to two well-studied problems in the domain of coupler-curve synthesis. A comparison of the results vis-a-vis existing ones shows that the proposed enhancements of the basic scheme of ${\texttt{\rm NSGA-II}}$ deliver promising improvements in terms of accuracy, and rate of convergence of the solutions.

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Citations
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Journal ArticleDOI

Comparative Study on the Synthesis of Path-Generating Four-Bar Linkages Using Metaheuristic Optimization Algorithms

TL;DR: Three improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods and were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems.
Journal ArticleDOI

Mathematical Construction for Mechanism Synthesis using Motion Generation Function

TL;DR: In this article , a new mathematical approach to solve the mechanism synthesis using motion generation function is presented and the proposed method will study how far the proposed mathematical model (i.e. matrix inversion) will ensure the mechanism to go through prescribed poses or try to approximate them with tolerable error.
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Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line

TL;DR: In this paper , an open-source simulation tool (JaamSim) was used to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility.
References
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Journal ArticleDOI

A fast and elitist multiobjective genetic algorithm: NSGA-II

TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Journal ArticleDOI

An efficient constraint handling method for genetic algorithms

TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
Journal ArticleDOI

Exact robot navigation using artificial potential functions

TL;DR: A methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design is presented.
Journal ArticleDOI

Optimal synthesis of mechanisms with genetic algorithms

TL;DR: The main advantages of the solution methods of optimal synthesis of planar mechanisms are its simplicity of implementation and its fast convergence to optimal solution, with no need of deep knowledge of the searching space.
Journal ArticleDOI

Performance of EAs for four-bar linkage synthesis

TL;DR: Three different evolutionary algorithms such as (GA), (PSO) and (DE) have been applied for synthesis of a four-bar mechanism minimising the error between desired and obtained coupler curve and performance of DE is found to be the best.
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