scholarly journals Novel search space updating heuristics-based genetic algorithm for optimizing medium-scale airline crew pairing problems

2017 ◽  
Vol 10 (1) ◽  
pp. 1082 ◽  
Author(s):  
Nihan Çetin Demirel ◽  
Muhammet Deveci
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Medium Scale ◽  
Airline Crew ◽  
Crew Pairing

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.

An Interactive Visualization of Genetic Algorithm on 2-D Graph

2012 ◽  
Vol 4 (1) ◽  
pp. 34-54 ◽  
Author(s):  
Humera Farooq ◽  
Nordin Zakaria ◽  
Muhammad Tariq Siddique
Keyword(s):  
Genetic Algorithm ◽  
Growth Process ◽  
Search Space ◽  
Human Intervention ◽  
Branching Structures ◽  
Multidimensional Search ◽  
L System ◽  
User Fatigue ◽  
Search Data ◽  
Selection Of

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

Genetic Algorithm-Based Searching Method for Piping Path Routing in Coal-Fired Boiler Buildings

2011 ◽  
Author(s):  
Naohiro Kusumi ◽  
David E. Goldberg ◽  
Noriyuki Ichinose
Keyword(s):  
Genetic Algorithm ◽  
Computer Aided Design ◽  
Steel Structures ◽  
Search Space ◽  
Design System ◽  
Dynamic Programming Method ◽  
Plant Design ◽  
Technical Specifications ◽  
The Many ◽  
Searching Method

Power plant design using digital engineering based on 3-D computer-aided design has become a mainstream technology because of possessing higher speed and improvement in design accuracy. To take a coal-fired boiler building as an example, it has many complex structures with several million parts including the boiler itself, large fans, steel structures, and piping in varying sizes. Therefore, it is not easy to maintain integrity of the whole design throughout all the many engineering processes. We have developed a smart design system for coal-fired boiler buildings to solve the integrity problem. This system is capable of creating and allocating 3-D models automatically in accordance with various technical specifications and engineering rules. Lately, however, there has been a growing demand for more effectiveness of the developed system. We have begun to look into the feasibility of further improvements of the system function. The first point to note, when considering effectiveness, is the piping path routing process in the coal-fired boiler building. The quantity of piping is large, and it has a considerable impact on performance of the whole plant because hot steam is fed into the steam turbine and cold steam is taken from it through the piping. A considerable number of studies have been made on automatic searching methods of piping path routing. Although, the decision of piping path routing by using the Dynamic Programming method is most commonly, a previously decided routing becomes an interference object because of the single searching method. Therefore, basically, the later the order of the routing becomes, the longer the length of the routing becomes. To overcome this problem, in this paper we have proposed a new searching method based on the Genetic Algorithm (GA). The GA is a multipoint searching algorithm based on the mechanics of natural selection and natural genetics. Virtual prohibited cells are introduced into the proposed search method as a new idea. The virtual prohibited cells are located in a search space. The different paths are generated by avoiding the virtual prohibited cells while searching for the piping path routing. The optimum locations of the prohibited cells which are expressed in a genotype are obtained by using the GA in order to get a lot of paths independent of the order of the routing. The proposed method was evaluated using a simple searching problem. The results showed that many effective paths are generated by making the virtual prohibited cells.

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.

Alien Genetic Algorithm for Exploration of Search Space

2010 ◽  
Author(s):  
Narendra Patel ◽  
Nitin Padhiyar ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
Suchismita Roy
Keyword(s):  
Genetic Algorithm ◽  
Search Space

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.

Airline Crew Pairing Optimization

2013 ◽  
pp. 1-22 ◽  
Author(s):  
Laurent Alfandari ◽  
Anass Nagih
Keyword(s):  
Airline Crew ◽  
Crew Pairing

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.

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