Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

Global Path Planning for Full-Area Coverage Robotic Systems by Employing an Active Genetic Algorithm

2011 ◽  
Vol 328-330 ◽  
pp. 1881-1886
Author(s):  
Cen Zeng ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
High Performance ◽  
Search Space ◽  
Area Coverage ◽  
Robotic Systems ◽  
Genetic Operators ◽  
A Algorithm ◽  
Coverage Path Planning ◽  
Search Spaces

Genetic algorithm (GA), a kind of global and probabilistic optimization algorithms with high performance, have been paid broad attentions by researchers world wide and plentiful achievements have been made.This paper presents a algorithm to develop the path planning into a given search space using GA in the order of full-area coverage and the obstacle avoiding automatically. Specific genetic operators (such as selection, crossover, mutation) are introduced, and especially the handling of exceptional situations is described in detail. After that, an active genetic algorithm is introduced which allows to overcome the drawbacks of the earlier version of Full-area coverage path planning algorithms.The comparison between some of the well-known algorithms and genetic algorithm is demonstrated in this paper. our path-planning genetic algorithm yields the best performance on the flexibility and the coverage. This meets the needs of polygon obstacles. For full-area coverage path-planning, a genotype that is able to address the more complicated search spaces.

Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm

2021 ◽  
Author(s):  
Che-Hang Cliff Chan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Parameters Identification ◽  
Experimental Results ◽  
Adaptive Search ◽  
Real Coded Genetic Algorithm ◽  
Hysteresis Modeling ◽  
Convergence Performance ◽  
Solution Accuracy

The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The propsed GAASS method has bee hybridized to a real-coded genetic algorithm to perform hysteresis parameters identification and hystereis invers compensation of an electromechanical-valve acuator installed on a pneumatic system. The experimental results have demonstrated the supreme performance of the proposed GAASS in the search of optimum solutions.

Genetic Algorithms for Decision-Making in Cognitive Radio Networks

2013 ◽  
pp. 247-263
Author(s):  
Tommy Hult ◽  
Abbas Mohammed
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Cognitive Radio ◽  
Search Space ◽  
Radio Spectrum ◽  
Dynamic Decision Making ◽  
Optimal Point ◽  
Optimization Search

Efficient use of the available licensed radio spectrum is becoming increasingly difficult as the demand and usage of the radio spectrum increases. This usage of the spectrum is not uniform within the licensed band but concentrated in certain frequencies of the spectrum while other parts of the spectrum are inefficiently utilized. In cognitive radio environments, the primary users are allocated licensed frequency bands while secondary cognitive users dynamically allocate the empty frequencies within the licensed frequency band according to their requested QoS (Quality of Service) specifications. This dynamic decision-making is a multi-criteria optimization problem, which the authors propose to solve using a genetic algorithm. Genetic algorithms traverse the optimization search space using a multitude of parallel solutions and choosing the solution that has the best overall fit to the criteria. Due to this parallelism, the genetic algorithm is less likely than traditional algorithms to get caught at a local optimal point.

Optimization of Radial Cams with Translating Roller Follower through Genetic Algorithms

2015 ◽  
Vol 783 ◽  
pp. 83-94
Author(s):  
Alberto Borboni
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Contact Pressure ◽  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Process ◽  
Radius Of Curvature ◽  
Design Problems ◽  
Computational Optimization ◽  
Different Types

In this work, the optimization problem is studied for a planar cam which rotates around its axis and moves a centered translating roller follower. The proposed optimization method is a genetic algorithm. The paper deals with different design problems: the minimization of the pressure angle, the maximization of the radius of curvature and the minimization of the contact pressure. Different types of motion laws are tested to found the most suitable for the computational optimization process.

THE DYNAMICS OF A CHANGING RANGE GENETIC ALGORITHM UNDER STABILIZING SELECTION

2009 ◽  
Vol 20 (07) ◽  
pp. 1063-1079
Author(s):  
ADIL AMIRJANOV
Keyword(s):  
Genetic Algorithm ◽  
Population Biology ◽  
Equations Of Motion ◽  
Search Space ◽  
Stabilizing Selection ◽  
Genetic Operators ◽  
Space Size ◽  
Final Population ◽  
Unique System ◽  
Average Fitness

The formalism is presented for modeling of a genetic algorithm (GA) with an adjustment of a search space size. The formalism for modeling of GA with an adjustment of a search space size assumes that the environment and the population form a unique system. In this paper, the formalism is applied to a problem which exhibits an interesting dynamics reminiscent of stabilizing selection in population biology. The equations of motion was derived that expressed the macroscopic statistical properties of population after reproductive genetic operators and an adjustment of a search space size in terms of those prior to the operation. Predictions of the theory are compared with experiments and are shown to predict the average fitness and the variance fitness of the final population accurately.

scholarly journals Constrained Novelty Search: A Study on Game Content Generation

2015 ◽  
Vol 23 (1) ◽  
pp. 101-129 ◽  
Author(s):  
Antonios Liapis ◽  
Georgios N. Yannakakis ◽  
Julian Togelius
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Selection Method ◽  
Search Algorithms ◽  
Genetic Operators ◽  
Search Methods ◽  
Search Spaces ◽  
Novelty Search ◽  
Feasible Space ◽  
Content Generation

Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.

scholarly journals Application of Multilayer Perceptron Genetic Algorithm Neural Network in Chinese-English Parallel Corpus Noise Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Bing Li ◽  
Anxie Tuo ◽  
Hanyue Kong ◽  
Sujiao Liu ◽  
Jia Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Recognition Rate ◽  
Solution Space ◽  
Search Space ◽  
Comparison Method ◽  
Parallel Corpus ◽  
Search Mechanism ◽  
Multiple Regions

This paper uses neural network as a predictive model and genetic algorithm as an online optimization algorithm to simulate the noise processing of Chinese-English parallel corpus. At the same time, according to the powerful random global search mechanism of genetic algorithm, this paper studied the principle and process of noise processing in Chinese-English parallel corpus. Aiming at the task of identifying isolated words for unspecified persons, taking into account the inadequacies of the algorithms in standard genetic algorithms and neural networks, this paper proposes a fast algorithm for training the network using genetic algorithms. Through simulation calculations, different characteristic parameters, the number of training samples, background noise, and whether a specific person affects the recognition result were analyzed and discussed and compared with the traditional dynamic time comparison method. This paper introduces the idea of reinforcement learning, uses different reward mechanisms to solve the inconsistency of loss function and evaluation index measurement methods, and uses different decoding methods to alleviate the problem of exposure bias. It uses various simple genetic operations and the survival of the fittest selection mechanism to guide the learning process and determine the direction of the search, and it can search multiple regions in the solution space at the same time. In addition, it also has the advantage of not being restricted by the restrictive conditions of the search space (such as differentiable, continuous, and unimodal). At the same time, a method of using English subword vectors to initialize the parameters of the translation model is given. The research results show that the neural network recognition method based on genetic algorithm which is given in this paper shows its ability of quickly learning network weights and it is superior to the standard in all aspects. The performance of the algorithm in genetic algorithm and neural network, with high recognition rate and unique application advantages, can achieve a win-win of time and efficiency.

scholarly journals Computing strong metric dimension of some special classes of graphs by genetic algorithms

2008 ◽  
Vol 18 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Jozef Kratica ◽  
Vera Kovacevic-Vujcic ◽  
Mirjana Cangalovic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graph Coloring ◽  
Crew Scheduling ◽  
Metric Dimension ◽  
Hard Problem ◽  
Genetic Operators ◽  
Strong Metric Dimension ◽  
Np Hard Problem ◽  
Special Classes

In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.

LZW Encoding in Genetic Algorithm

2011 ◽  
Vol 2 (4) ◽  
pp. 25-36 ◽  
Author(s):  
Worasait Suwannik
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Original Problem ◽  
Search Space ◽  
Binary String ◽  
Exponential Rate

To solve a problem using Genetic Algorithms (GAs), a solution must be encoded into a binary string. The length of the binary string represents the size of the problem. As the length of the binary string increases, the size of the search space also increases at an exponential rate. To reduce the search space, one approach is to use a compressed encoding chromosome. This paper presents a genetic algorithm, called LZWGA, that uses compressed chromosomes. An LZWGA chromosome must be decompressed using an LZW decompression algorithm before its fitness can be evaluated. By using compressed encoding, the search space is reduced dramatically. For one-million-bit problem, the search space of the original problem is 21000000 or about 9.90x10301029 points. When using a compressed encoding, the search space was reduced to 8.37x10166717 points. LZWGA can solve one-million-bit OneMax, RoyalRoad, and Trap functions.

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