Application of Multi-objective Genetic Algorithm (MOGA) Optimization in Machining Processes

Zolpakar, Nor Atiqah; Lodhi, Swati Singh; Pathak, Sunil; Sharma, Mohita Anand

doi:10.1007/978-3-030-19638-7_8

Cited by 43 publications

(19 citation statements)

References 28 publications

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“…54 MOGA is very relevant to solve the problem of the differences in optimisation objectives. 55 For example, a problem occurred when a research objective of minimising cost and increment of production volume contradicting each other, thus requiring the application of a multi-objective optimisation method. 39…”

Section: Optimisation Methods Used For Alb With Energy Considerationmentioning

confidence: 99%

A review of assembly line balancing optimisation with energy consideration using meta-heuristic algorithms

Ramli

Rashid

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Energy utilisation is one of the global trending issues. Various approaches have been introduced to minimise energy utilisation especially in the manufacturing sector, which is the largest consumer sector. One of the approaches includes the consideration of energy utilisation in the Assembly Line Balancing (ALB) optimisation. This paper reviews the ALB with energy consideration from 2012 to 2020. The selected articles were limited to problems solved using meta-heuristic algorithms. The review mainly focusses on the soft computing aspect such as problem variant, optimisation objectives, energy modelling and optimisation algorithm for ALB with energy consideration. Based on the review, the ALB with energy consideration was able to reduce energy utilisation up to 11.9%. It was found that the contribution in future ALB with energy research will be human-oriented, either social factor consideration in optimisation or energy utilisation modelling for workers. In addition, the effort to introduce an algorithm with efficient performance must be pursued because ALB problems have become more complicated. The findings from this review could assist future researchers to align their research direction with the observed trend. This paper also provides the research gap and research opportunities in the future.

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Section: Optimisation Methods Used For Alb With Energy Considerationmentioning

confidence: 99%

A review of assembly line balancing optimisation with energy consideration using meta-heuristic algorithms

Ramli

Rashid

2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…e multiobjective genetic algorithm is a guided random search method that is suitable for solving multiobjective optimization problems as it can explore the diverse regions of the solution space [23]. e solution to MOGA is illustrated by using the Pareto fronts based on the natural biological evaluation principle.…”

Section: Optimization Of Sss-7mentioning

confidence: 99%

“…Shan et al [20] proposed an assembly method of the modular body through decomposing a structure, and then a simulation was conducted to obtain the design variables via the NSGA-II algorithm. Dama et al [21][22][23] proposed an improved GA optimizer and MOGA algorithm for shape optimization of a car body. Deng et al [24][25][26] applied an evolution algorithm to a complex optimization problem which had proved effective in solving an actual engineering optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Research on Torsional Property of Body-In-White Based on Square Box Model and Multiobjective Genetic Algorithm

Meng

Yuan

Zhao

et al. 2021

Mathematical Problems in Engineering

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In order to assess the performance of a vehicle in the conceptual design stage, a square box model was proposed to predict the torsional stiffness and the first-order torsional frequency of Body-in-White. The structure of Body-in-White was decomposed into eight simple structural surfaces, from which a square box model was constructed. Based on the finite element method, modified shear stiffness of each simple structure surface was calculated and the torsional stiffness was obtained. Then, simple structural surfaces of Body-in-White were constructed into an eight degree-of-freedom series spring system to calculate the first-order torsional frequency. Furthermore, a multiobjective genetic algorithm was used to determine the thickness and structural reinforcement of panels with small stiffness, so as to achieve the goal of increasing the stiffness while reducing the mass of the panel. The result shows that the optimal values of thickness are reduced by around 9.9 percent without affecting their performance by the proposed method. Compared to the prediction results obtained with the complicated numerical simulation, the relative error of the square box model in predicting the torsional stiffness is 6.04 percent and in predicting the first-order torsional frequency is 0.95 percent, indicating that the prediction model is effective.

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“…16 Multiobjective genetic algorithm (MGA), on the other hand, is an MO approach where optimization is performed via exploration at various zones of the solution space. 17 The main advan-tage brought by genetic algorithms, compared with conventional optimization algorithms, is that they process a population of solutions in each iteration, instead of a single solution, which is beneficial in obtaining multiple optimal solutions instead of a single one. 18 This optimization technique can be applied in MATLAB thanks to the embedded algorithm, "gamultiobj."…”

Section: Introductionmentioning

confidence: 99%

Kinetic modeling of Fischer–Tropsch‐to‐olefins process via advanced optimization

Turan

Ataç

Kurucu

et al. 2021

Int J of Chemical Kinetics

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The reaction kinetics of a Fischer–Tropsch (FT) process to produce lower olefins was modeled utilizing the experimental data produced using an in‐house synthesized iron‐based catalyst. Along with FT chain growth reaction that is assumed to follow alkyl mechanism, water–gas shift reaction was also taken into consideration due to its significance. Not only the rate constants but also apparent activation energies were obtained via an integrated approach utilizing multiobjective and constrained nonlinear minimization methods in order to define a model valid at a temperature range instead of a single point. The adaption of a hybrid optimization method utilizing both population‐ and individual‐based techniques enhanced prediction accuracy compared with the case where only multiobjective genetic algorithm is used. Thanks to the developed model, the effect of process parameters on product distribution was investigated. Finally, the kinetic model was compared with Anderson–Schulz–Flory model and the deviations observed were discussed.

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Application of Multi-objective Genetic Algorithm (MOGA) Optimization in Machining Processes

Cited by 43 publications

References 28 publications

A review of assembly line balancing optimisation with energy consideration using meta-heuristic algorithms

A review of assembly line balancing optimisation with energy consideration using meta-heuristic algorithms

Research on Torsional Property of Body-In-White Based on Square Box Model and Multiobjective Genetic Algorithm

Kinetic modeling of Fischer–Tropsch‐to‐olefins process via advanced optimization

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