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Journal ArticleDOI

Genetic Algorithm and its Applications to Mechanical Engineering: A Review

TL;DR: Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
About: This article is published in Materials Today: Proceedings.The article was published on 2015-01-01. It has received 82 citations till now. The article focuses on the topics: Meta-optimization & Genetic representation.
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Journal ArticleDOI
TL;DR: A new framework to optimize the geometry and topology of inner and outer support structures of Additive Manufacturing technologies, validated on several test cases of various geometries, containing both inner andouter areas to be supported.
Abstract: The emergence and improvement of Additive Manufacturing technologies allow the fabrication of complex shapes so far inconceivable. However, to produce those intricate geometries, support structures are required. Besides wasting unnecessary material, these structures are consuming valuable production and post-processing times. This paper proposes a new framework to optimize the geometry and topology of inner and outer support structures. Starting from a uniform lattice structure filling both the inner and outer areas to be supported, the approach removes a maximum number of beams so as to minimize the volume of the support. The most suited geometry for the initial lattice structure is defined at the beginning considering the possibilities of the manufacturing technologies. Then, the pruning of the structure is performed through a genetic algorithm, for which the control parameters values have been tuned through a design of experiments. The proposed approach is validated on several test cases of various geometries, containing both inner and outer areas to be supported. The generated support structures are compared to the ones obtained by several state-of-the-art support structure strategies and are proved to have lower material consumption.

63 citations

Journal ArticleDOI
TL;DR: The inclusion of the inheritance operator improves the speed of convergence to global Pareto-optimal front significantly with a minimum number of generations over existing NSGA-II and several JG adapted NSGA, and is established by solving real-life robust multi-objective optimization problems involving the drilling of oil-well and synthesis of sal oil biodiesel.

55 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a novel adaptive decoding biased random key genetic algorithm for cloud workflow scheduling, where the improved real number coding based on random key with limited value range is employed, and some novel schemes such as the population initialization based on level and heuristics including dynamic heterogeneous earliest finish time, the dynamic adaptive decoding, the load balance with communication avoidance and iterative forward-backward scheduling are designed for population initialization, chromosome decoding and improvement.

41 citations

Journal ArticleDOI
TL;DR: Two intelligent algorithms for welding path optimization, genetic algorithm (GA) and discrete particle swarm optimization, are proposed to optimize the welding robot path and the GA achieves the fastest iterative efficiency.
Abstract: To make the welding robot more reasonable and furthermore improve the productivity and reduce costs, two intelligent algorithms for welding path optimization, genetic algorithm (GA) and discrete particle swarm optimization, are proposed to optimize the welding robot path. Through the improved selection of the operator, the GA achieves the fastest iterative efficiency. By introducing the “swap operator” and “swap sequence” in the particle swarm optimization algorithm, the PSO algorithm is improved for the solution of the discrete problem (welding robot path planning) which is superior to the continuous optimization problem. Besides, for the better iterative efficiency of PSO, the parameters of traditional inertia weight are determined by a linear inertia weigh, which can improve the convergence performance of the algorithm. The modeling and solutions of the two algorithms are discussed in detail to illustrate the applications in the welding robot path optimization. In order to compare the pros and cons of the two algorithms, the same welding tasks are presented, and Matlab simulation is carried out. The simulation results show that both genetic algorithm and particle swarm optimization algorithm can obtain the optimal or near-optimal welding path by iterative calculations.

41 citations


Cites background from "Genetic Algorithm and its Applicati..."

  • ...Therefore, OC is chosen as the crossover operator [20]....

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Journal ArticleDOI
TL;DR: Among several machine learning techniques reviewed in this study, application of artificial neural networks (ANN) in process modelling and optimization has become quite noticeable because of its ability to predict the output quickly and accurately.

37 citations

References
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Journal ArticleDOI
TL;DR: A genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability is proposed.
Abstract: This paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. Initially, the programming algorithm (PA) was designed and implemented, taking into account all constraints mentioned. This algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time. Once the programming algorithm was defined, the genetic algorithm (GA) was developed for minimizing two objectives: delivery times and total programming cost. The stages defined for this algorithm were: selection, crossover and mutation. During the first stage, the individuals composing the next generation are selected using a strong dominance test. Given the strong restrictions on the model, the crossover stage utilizes a process level structure (PLS) where processes are grouped by its levels in the product tree. Finally during the mutation stage, the solutions are modified in two different ways (selected in a random fashion): changing the selection of the resources of one process and organizing the processes by its execution time by level. In order to obtain more variability in the found solutions, the production orders and the products are organized with activity planning rules such as EDD, SPT and LPT. For each level of processes, the processes are organized by its processing time from lower to higher (PLU), from higher to lower (PUL), randomly (PR), and by local search (LS). As strategies for local search, three algorithms were implemented: Tabu Search (TS), Simulated Annealing (SA) and Exchange Deterministic Algorithm (EDA). The purpose of the local search is to organize the processes in such a way that minimizes the total execution time of the level. Finally, Pareto fronts are used to show the obtained results of applying each of the specified strategies. Results are analyzed and compared.

9 citations

Journal ArticleDOI
TL;DR: In this article, an attempt has been made for optimization of the design variables of a PPHE so that effectiveness of the heat exchanger per unit volume is maximized under the constraints of fluid pressure drop and length of the Heat Exchanger.
Abstract: Perforated plate heat exchangers (PPHEs) come under the category of compact heat exchanger; offering high effectiveness, large surface area per unit volume (as high as 6000 m2/m3) and better flow characteristics. PPHEs are constructed of alternately arranged perforated plates and spacers. Heat exchanging fluids flow through the holes of the plates and exchange heat by conduction through the plate material. Spacers help minimizing axial conduction and reheadering of fluids intermittently. Design of a compact heat exchanger is targeted for high effectiveness, low volume and minimum pressure drop. Performance of a PPHE depends on many design variables such as plate thickness, spacer thickness, pore diameter, porosity etc. For a given heat duty, these parameters can be optimized for maximizing effectiveness, minimizing volume and minimizing or limiting pressure drop.In this paper an attempt has been made for optimization of the design variables of a PPHE so that effectiveness of the heat exchanger per unit volume is maximized under the constraints of fluid pressure drop and length of the heat exchanger. Unlike the conventional approach, importance is given to the length of the heat exchanger which is limited to the available space inside the vacuum chamber of the diffusion bounding machine or the space available in a specific application. Using the given length of the heat exchanger and allowable pressure drop, the problem has been defined in unconstrained form and solved by Genetic algorithm.

5 citations

Journal Article
TL;DR: In this paper, the optimization of the condition-based maintenance (CBM) applied on manufacturing multi-equipment system under cost and benefit criteria is addressed by means of the application of a multi-objective evolutionary algorithm.
Abstract: Purpose: This paper deals with the optimization of the condition based maintenance (CBM) applied on manufacturing multi-equipment system under cost and benefit criteria. Design/methodology/approach: The system is modeled using Discrete Event Simulation (DES) and optimized by means of the application of a Multi-Objective Evolutionary Algorithm (MOEA). Findings: Solution for the joint optimization of the condition based maintenance model applied on several equipment has been obtained. Research limitations/implications: The developed approach has been successfully applied to the optimization of condition based maintenance activities of a hubcap production system composed by three plastic injection machines and a painting station, for management decision support. Originality/value: This paper provides a solution for the joint optimization of CBM strategies applied on several equipments.

4 citations

01 Jan 2014
TL;DR: In this paper, the authors provide background information, motivation for application and an exposition to the methodologies employed in the development of soft computing technologies in engineering, and provide a systematic review of the literature originating from these efforts which focus on materials engineering (ME) particularly sheet metals.
Abstract: Within the last three decades, a solid and real amount of research efforts has been directed at the application of soft computing (SC) techniques in engineering. This paper provides a systematic review of the literature originating from these efforts which focus on materials engineering (ME) particularly sheet metals. The primary aim is to provide background information, motivation for application and an exposition to the methodologies employed in the development of soft computing technologies in engineering. Our review shows that all the works on the application of SC to sheet metal have reported excellent, good, positive or at least encouraging results. Our appraisal of the literature suggest that the interface between material engineering and intellectual systems engineering techniques, such as soft computing, is still blur. The need to formalize the computational and intelligent system engineering methodology used in sheet material, therefore, arises. We also provide a brief exposition to our on-going efforts in this direction. Although our study focuses on materials engineering in particular, we think that our findings applies to other areas of engineering as well.

2 citations

Journal ArticleDOI
28 May 2012
TL;DR: In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc.
Abstract: Forming is a compression-tension process involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection of various parameters is still based on trial and error methods. In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc. The optimization problem has been formulated with the objective of optimizing the maximum forming load required in Forming. Genetic algorithm is used for the optimization purpose to minimize the drawing load and to optimize the process parameters. A finite element analysis simulation software Fast Form Advanced is used for the validations of the results after optimization.

2 citations