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: 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
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
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
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..."
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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 Article•
TL;DR: In this article, the authors proposed a GA-based solution to the multi-depot vehicle routing problem (MDVRP), where the customers are grouped based on distance to their nearest depots and then routed with Clarke and Wright saving method.
Abstract: The Multi-Depot Vehicle Routing Problem (MDVRP), an extension of classical VRP, is a NP-hard problem for simultaneously determining the routes for several vehicles from multiple depots to a set of customers and then return to the same depot. The objective of the problem is to find routes for vehicles to service all the customers at a minimal cost in terms of number of routes and total travel distance, without violating the capacity and travel time constraints of the vehicles. The solution to the MDVRP, in this paper, is obtained through Genetic Algorithm (GA). The customers are grouped based on distance to their nearest depots and then routed with Clarke and Wright saving method. Further the routes are scheduled and optimized using GA. A set of five different Cordeau’s benchmark instances (p01, p02, p03, p04, p06) from the online resource of University of Malaga, Spain were experimented using MATLAB R2008b software. The results were evaluated in terms of depot’s route length, optimal route, optimal distance, computational time, average distance, and number of vehicles. Comparison of the experimental results with state-of-the-art techniques shows that the performance of GA is feasible and effective for solving the multi-depot vehicle routing problem.
68 citations
TL;DR: In this paper, a pareto-converging genetic algorithm (PCGA) was used to locate the optima of a continuous caster in the mold region of a steel mill.
62 citations
Journal Article•
TL;DR: Comparison of the experimental results with state-of-the-art techniques shows that the performance of GA is feasible and effective for solving the multi-depot vehicle routing problem.
58 citations
TL;DR: In this paper, the authors present the application of the stress-strength distribution interference theory to calculate the reliability of gear transmission, establishes the Kriging model for function fitting, and uses genetic algorithm to globally optimize the volume and reliability of large ball mill gear transmission.
49 citations
TL;DR: In this article, the optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina-based ceramic cutting tools is presented.
Abstract: Alumina-based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. While the initial cost of alumina based ceramic inserts is generally higher than carbide or cermet inserts, the cost per part machined is often lower. Production cost is the main concern of the industry and it has to be optimised to fully utilize the advantages of ceramic cutting tools. In this study, optimization of machining parameters on machining S.G. iron (ASTM A536 60-40-18) using alumina based ceramic cutting tools is presented. Before doing the optimization work, experimental machining study is carried out using Ti [C,N] mixed alumina ceramic cutting tool (CC 650) and Zirconia toughened alumina ceramic cutting tool (Widialox G) to get actual input values to the optimization problem, so that the optimized results will be realistic. The optimum machining parameters are found out using Genetic algorithm and it is found that Widialo...
32 citations