A new multi-response Jaya Algorithm for optimisation of EDM process parameters

Agarwal, Neeraj; Pradhan, Mohan Kumar; Shrivastava, Nitin

doi:10.1016/j.matpr.2018.10.167

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…where Aj,b,i and Aj,w,i is the input variable j for the corresponding best and worst function at i th iteration. A' j,k,i is the modified solution of A j, k, i and r1,j,i and r2,j,i are two random numbers [21]. The Random numbers within the range of input variables are considered and corresponding combined objective function (COF -Equation ( 8)) is calculated and tabulated in Table 7 for a population size of 6.…”

Section: Formulation Of Multi Objective Functionmentioning

confidence: 99%

Mathematical Modeling & Multi Response Optimization for Improving Machinability of Alloy Steel Using RSM, GRA and Jaya Algorithm

2021

IJE

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Section: Formulation Of Multi Objective Functionmentioning

confidence: 99%

Mathematical Modeling & Multi Response Optimization for Improving Machinability of Alloy Steel Using RSM, GRA and Jaya Algorithm

2021

IJE

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“…Multiple regression modeling was used to combine the responses as a single-objective function. Then, a JA algorithm was used to determine an optimal set to obtain the desired outputs (maximum MRR along with minimum SR simultaneously) [19].…”

Section: State Of the Artmentioning

confidence: 99%

Using combined artificial neural network and particle swarm optimization algorithm for modeling and optimization of electrical discharge machining process

Moghaddam

Kolahan

2019

Scientia Iranica

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In this study, the Electrical Discharge Machining (EDM) process, which is extensively employed in di erent manufacturing processes such as mold/die making industries, was modeled and optimized using Arti cial Neural Network (ANN) and Particle Swarm Optimization (PSO) algorithm. Surface quality, material removed from the workpiece, and tool erosion ratio were considered as the performance characteristics of this process. The objective of this study comprises the optimization of the process in order to nd a combination of process input parameters to simultaneously minimize Tool Wear Rate (TWR) and Surface Roughness (SR) and maximize Material Removal Rate (MRR). By establishing a relationship between the process input parameters and the output characteristics, a neural network with back propagation algorithm (BPNN) was used. In the last section of this research, PSO algorithm was used for the optimization of the process with multi-response characteristics. By verifying the accuracy of the proposed optimization procedure, a set of con rmation tests was carried out. Results showed that the proposed modeling method (BPNN) could accurately simulate the authentic EDM process with less than 1% error. Furthermore, the optimization technique (PSO algorithm) is quite e cient in process optimization (with less than 4% error).

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“…The Jaya algorithm (technically, heuristic or meta-heuristic) [1,2] is one of the newest members of the evolutionary computation family. This algorithm and its variants have been highly successful in global optimization in continuous domains and have seen wide applicability in diverse areas, including engineering [3][4][5], manufacturing [6], energy [7], fuel cells [8], healthcare [9] and finance [10]. A recent survey can be found in [11].…”

Section: Introductionmentioning

confidence: 99%

Stochastic models of Jaya and semi-steady-state Jaya algorithms

Chakraborty¹

2022

Preprint

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We build stochastic models for analyzing Jaya and semi-steady-state Jaya algorithms. The analysis shows that for semi-steady-state Jaya (a) the maximum expected value of the number of worst-index updates per generation is a paltry 1.7 regardless of the population size; (b) regardless of the population size, the expectation of the number of best-index updates per generation decreases monotonically with generations; (c) exact upper bounds as well as asymptotics of the expected best-update counts can be obtained for specific distributions; the upper bound is 0.5 for normal and logistic distributions, ln 2 for the uniform distribution, and e −γ ln 2 for the exponential distribution, where γ is the Euler-Mascheroni constant; the asymptotic is e −γ ln 2 for logistic and exponential distributions and ln 2 for the uniform distribution (the asymptotic cannot be obtained analytically for the normal distribution). The models lead to the derivation of computational complexities of Jaya and semi-steady-state Jaya. The theoretical analysis is supported with empirical results on a benchmark suite. The insights provided by our stochastic models should help design new, improved population-based search/optimization heuristics.

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A new multi-response Jaya Algorithm for optimisation of EDM process parameters

Cited by 12 publications

References 8 publications

Mathematical Modeling & Multi Response Optimization for Improving Machinability of Alloy Steel Using RSM, GRA and Jaya Algorithm

Mathematical Modeling & Multi Response Optimization for Improving Machinability of Alloy Steel Using RSM, GRA and Jaya Algorithm

Using combined artificial neural network and particle swarm optimization algorithm for modeling and optimization of electrical discharge machining process

Stochastic models of Jaya and semi-steady-state Jaya algorithms

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