Journal ArticleDOI
Genetic Algorithm and its Applications to Mechanical Engineering: A Review
Trupti Bhoskar,Omkar Kulkarni,Ninad Kulkarni,Sujata Patekar,Ganesh Kakandikar,Vilas M. Nandedkar +5 more
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TLDR
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.read more
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Genetic-algorithm based framework for lattice support structure optimization in additive manufacturing
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.
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The elitist non-dominated sorting genetic algorithm with inheritance (i-NSGA-II) and its jumping gene adaptations for multi-objective optimization
Mithilesh Kumar,Chandan Guria +1 more
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.
Journal ArticleDOI
An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling
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.
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Research on Intelligent Welding Robot Path Optimization Based on GA and PSO Algorithms
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.
Journal ArticleDOI
Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review
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.
References
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Journal ArticleDOI
A general heuristic for vehicle routing problems
David Pisinger,Stefan Ropke +1 more
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.
Dissertation
A genetic algorithm for resource-constrained scheduling
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).
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A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem
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.
Journal ArticleDOI
Genetic algorithm-based optimization of cutting parameters in turning processes
Doriana M. D’Addona,Roberto Teti +1 more
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.
Journal ArticleDOI
Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm
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.