Optimal Synthesis of Four-Bar Linkage Path Generation through Evolutionary Computation with a Novel Constraint Handling Technique.

Suwin Sleesongsom,Sujin Bureerat

doi:10.1155/2018/5462563

Abstract

This paper presents a novel constraint handling technique for optimum path generation of four-bar linkages using evolutionary algorithms (EAs). Usually, the design problem is assigned to minimize the error between desired and obtained coupler curves with penalty constraints. It is found that the currently used constraint handling technique is rather inefficient. In this work, we propose a new technique, termed a path repairing technique, to deal with the constraints for both input crank rotation and Grashof criterion. Three traditional path generation test problems are used to test the proposed technique. Metaheuristic algorithms, namely, artificial bee colony optimization (ABC), adaptive differential evolution with optional external archive (JADE), population-based incremental learning (PBIL), teaching-learning-based optimization (TLBO), real-code ant colony optimization (ACOR), a grey wolf optimizer (GWO), and a sine cosine algorithm (SCA), are applied for finding the optimum solutions. The results show that new technique is a superior constraint handling technique while TLBO is the best method for synthesizing four-bar linkages.

Highlights

Since the last decade, many researchers have tried to solve the optimization for path generation of four-bar linkages using metaheuristic (MH) algorithms. e objective of path generation problem is to find dimensions of a mechanism, which minimize the target path and the actual path of a point on the coupler link
It was found that some MHs have been used for solving this task except the work by Sleesongsom and Bureerat [17]; one of the objectives of this paper is to present the comparative performance of a number of currently used MHs. ose algorithms include the artificial bee colony optimization (ABC), adaptive differential evolution with optional external archive (JADE), population-based incremental learning (PBIL), teaching-learning-based optimization (TLBO), the realcode ant colony optimization (ACOR), a grey wolf optimizer (GWO), a Jaya algorithm (Jaya), and a sine cosine algorithm (SCA)
Further discussion is provided in order to investigate the behavior of the proposed constraint handling technique path repairing algorithm (PRA) and why it is e cient when used with TLBO

Summary

Introduction

Many researchers have tried to solve the optimization for path generation of four-bar linkages using metaheuristic (MH) algorithms. e objective of path generation problem is to find dimensions of a mechanism, which minimize the target path and the actual path of a point on the coupler link. Many researchers have tried to solve the optimization for path generation of four-bar linkages using metaheuristic (MH) algorithms. A nongradient-based optimizer, e.g., evolutionary algorithms (EAs) or metaheuristics (MHs), is a more popular selection in solving such optimization problems. E use of gradient-based method, on the other hand, is somewhat questionable to deal with global optimization and nonsmooth constraints in the path synthesis. If those aforementioned factors can be alleviated, the advantages of the gradient-based method are better convergence rate and consistency. Many researchers have combined MHs and a gradient-based optimizer for solving many kinds of real world problems, which is called a hybrid algorithm. It was found that some MHs have been used for solving this task except the work by Sleesongsom and Bureerat [17]; one of the objectives of this paper is to present the comparative performance of a number of currently used MHs. ose algorithms include the artificial bee colony optimization (ABC), adaptive differential evolution with optional external archive (JADE), population-based incremental learning (PBIL), teaching-learning-based optimization (TLBO), the realcode ant colony optimization (ACOR), a grey wolf optimizer (GWO), a Jaya algorithm (Jaya), and a sine cosine algorithm (SCA)

Objectives

Methods

Conclusion