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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TL;DR: In this article, a pre-reforming of propane was studied over an industrial nickel-chromium catalyst under pressures of 1 and 5 bar, at a low steam to carbon molar ratio of 1, in the temperature range of 220-380 °C and at flow rates of 4000 and 12,000 h−1.
Abstract: Pre-reforming of propane was studied over an industrial nickel-chromium catalyst under pressures of 1 and 5 bar, at a low steam to carbon molar ratio of 1, in the temperature range of 220–380 °C and at flow rates of 4000 and 12,000 h−1. It was shown that propane conversion proceeded more efficiently at low pressure (1 atm) and temperatures above 350 °C. A genetic algorithm was applied to search for kinetic parameters better fitting experimental results in such a wide range of experimental conditions. Power law and Langmuir–Hinshelwood kinetics were considered. It was shown that only Langmuir–Hinshelwood type kinetics correctly described the experimental data and could be used to simulate the process of propane pre-reforming and predict propane conversion under the given reaction conditions. The significance of Langmuir–Hinshelwood kinetics increases under high pressure and temperatures below 350 °C.
11 citations
TL;DR: The obtained manipulators through the proposed method meet the requirements of workspace while also showing an improved performance in terms of dexterity, forces transmission and stiffness.
Abstract: In this article, we propose a method for the optimal dimensional synthesis of planar parallel manipulators covering a specified workspace. The proposed method aims to reduce the dimensions of the l...
11 citations
Cites background from "Genetic Algorithm and its Applicati..."
...Bhoskar et al. (2015) presented a review of case studies on the use of GA within the mechanical engineering field....
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TL;DR: A modified non-dominated sorting genetic algorithm II is proposed to not only optimize the control allocation between the aileron and rudder channels on different flying quality levels but also explore the relationships between the optimum solutions and the state variables of the aircraft.
Abstract: Coordinated aileron and rudder control is crucial to the lateral control stability augmentation of an aircraft. In this paper, a modified non-dominated sorting genetic algorithm II is proposed to not only optimize the control allocation between the aileron and rudder channels on different flying quality levels but also explore the relationships between the optimum solutions and the state variables of the aircraft. In doing so, a digital, nets-based stratification method is used to initialize the search chromosomes more evenly. To improve the search efficiency of the algorithm, crowding-distance-based interpolation and elimination strategies are developed to approach the optimum Pareto frontier as close as possible. Moreover, a dynamic depth search method is proposed to balance between the global and local explorations. Finally, the control allocation relationships between the aileron and rudder channels on different flying quality levels are illustrated. The comparative simulations on a six-degree-of-freedom Boeing 747 model are carried out to verify the feasibility of the proposed algorithm.
10 citations
Cites background from "Genetic Algorithm and its Applicati..."
...By mimicking natural evolutionary strategies to formulate the chromosomes’ updating procedure, the genetic algorithm based optimization strategy and its variations have been utilized widely in many fields including engineering, mathematics, computer science, and finance [20], [21]....
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TL;DR: A new WSN deployment method using a genetic algorithm (GA) is here proposed, which showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k.
Abstract: Wireless sensor networks (WSNs) are suitable for the continuous monitoring of crop information in large-scale farmland. The information obtained is great for regulation of crop growth and achieving high yields in precision agriculture (PA). In order to realize full coverage and k-connectivity WSN deployment for monitoring crop growth information of farmland on a large scale and to ensure the accuracy of the monitored data, a new WSN deployment method using a genetic algorithm (GA) is here proposed. The fitness function of GA was constructed based on the following WSN deployment criteria: (1) nodes must be located in the corresponding plots; (2) WSN must have k-connectivity; (3) WSN must have no communication silos; (4) the minimum distance between node and plot boundary must be greater than a specific value to prevent each node from being affected by the farmland edge effect. The deployment experiments were performed on natural farmland and on irregular farmland divided based on spatial differences of soil nutrients. Results showed that both WSNs gave full coverage, there were no communication silos, and the minimum connectivity of nodes was equal to k. The deployment was tested for different values of k and transmission distance (d) to the node. The results showed that, when d was set to 200 m, as k increased from 2 to 4 the minimum connectivity of nodes increases and is equal to k. When k was set to 2, the average connectivity of all nodes increased in a linear manner with the increase of d from 140 m to 250 m, and the minimum connectivity does not change.
10 citations
Cites background from "Genetic Algorithm and its Applicati..."
...Evolutionary algorithms are effective means of solving NP problem [63,64]....
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...Evolutionary algorithms are effective means of solving NP problems [63,64]....
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TL;DR: In this paper, an ultrasonic phased array (SEAR) was used for inspection of the engine cylinder cavity cavities to detect the corrosion defects on the engine cylinders, based on the ultrasonic phase.
Abstract: In the automotive remanufacturing movement, the inspection of the corrosion defects on the engine cylinder cavity is a key and difficult problem. In this article, based on the ultrasonic phased arr...
9 citations
Cites methods from "Genetic Algorithm and its Applicati..."
...Imitating the theory of ‘‘natural selection and survival of the fittest’’ in Darwinian evolution, the GA is a global optimization method which has been commendably applied to many optimization problems.(35) In this algorithm, fitness function is used to guide the search direction....
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References
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TL;DR: A unified heuristic which is able to solve five different variants of the vehicle routing problem and shown promising results for a large class of vehicle routing problems with backhauls as demonstrated in Ropke and Pisinger.
1,282 citations
Dissertation•
01 Jan 1996
TL;DR: Jakiela et al. as discussed by the authors presented a genetic algorithm approach to resource-constrained scheduling using a direct, time-based representation, which was applied to over 1000 small job shop and project scheduling problems (10-300 activities, 3-10 resource types).
Abstract: This work describes a genetic algorithm approach to resource-constrained scheduling using a direct, time-based representation. Whereas traditional solution methods are typically sequencebased, this representation encodes schedule information as a dual array of relative delay times and integer execution modes. The representation supports multiple execution modes, preemption, non-uniform resource availability/usage, a variety of resource types, probabilistic resource performance models, overlapping precedence relationships, and temporal constraints on both tasks and resources. In addition, the representation includes time-varying resource availabilities and requirements. Many objective measures can be defined such as minimization of makespan, maximization of net present value, or minimization of average tardiness. Multiple, possibly conflicting objectives are supported. The genetic algorithm adapts to dynamic factors such as changes to the project plan or disturbances in the schedule execution. In addition to the scheduling representation, this thesis presents a structured method for defining and evaluating multiple constraints and objectives. The genetic algorithm was applied to over 1000 small job shop and project scheduling problems (10-300 activities, 3-10 resource types). Although computationally expensive, the algorithm performed fairly well on a wide variety of problems. With little attention given to its parameters, the algorithm found solutions within 2% of published best in 60% of the project scheduling problems. Performance on the jobshop problems was less encouraging; in a set of 82 jobshop problems with makespan as the single performance measure, the algorithm found solutions with makespan 2 to 3 times the published best. On project scheduling problems with multiple execution modes, the genetic algorithm performed better than deterministic, bounded enumerative search methods for 10% of the 538 problems tested. The test runs were performed with minimal attention to tuning of the genetic algorithm parameters. In most cases, better performance is possible simply by running the algorithm longer or by varying the selection method, population size or mutation rate. However, the results show the flexibility and robustness of a direct representation and hint at the possibilities of integrating the genetic algorithm approach with other methods. Thesis Committee: Mark Jakiela , Associate Professor of Mechanical Engineering, MIT Woodie Flowers, Pappalardo Professor of Mechanical Engineering, MIT Stephen Graves, Professor of Management Science, Sloan School of Management Karl Ulrich, Associate Professor of Operations and Information Management, The Wharton School This document is available from ftp://lancet.mit.edu/pub/mbwall/phd/thesis.ps.gz effective 1 August 1996, Hunter Associate Professor of Mechanical Design and Manufacturing, Mechanical Engineering, Washington University, St. Louis.
185 citations
TL;DR: This paper considers the job-shop problem with release dates and due dates with the objective of minimizing the total weighted tardiness, and shows that the efficiency of genetic algorithms does no longer depend on the schedule builder when an iterated local search is used.
135 citations
TL;DR: An optimization paradigm based on GA for the determination of the cutting parameters in machining operations is proposed in this article, where the GA has been used as an optimal solution finder for finding optimal cutting parameters during a turning process.
114 citations
TL;DR: In this article, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives.
Abstract: The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented .
103 citations