A Genetic Algorithm Based Procedure for Extracting Optimal Solutions From a Morphological Chart

2007 ◽  
Author(s):  
Santosh Tiwari ◽  
Joshua Summers ◽  
Georges Fadel
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Mathematical Representation ◽  
Optimal Solutions ◽  
Proof Of Concept ◽  
Optimization Framework ◽  
Mutation Operators ◽  
Novel Approach ◽  
Morphological Chart ◽  
Crossover And Mutation

A novel approach using a genetic algorithm is presented for extracting globally satisfycing (Pareto optimal) solutions from a morphological chart where the evaluation and combination of “means to sub-functions” is modeled as a combinatorial multi-objective optimization problem. A fast and robust genetic algorithm is developed to solve the resulting optimization problem. Customized crossover and mutation operators specifically tailored to solve the combinatorial optimization problem are discussed. A proof-of-concept simulation on a practical design problem is presented. The described genetic algorithm incorporates features to prevent redundant evaluation of identical solutions and a method for handling of the compatibility matrix (feasible/infeasible combinations) and addressing desirable/undesirable combinations. The proposed approach is limited by its reliance on the quantifiable metrics for evaluating the objectives and the existence of a mathematical representation of the combined solutions. The optimization framework is designed to be a scalable and flexible procedure which can be easily modified to accommodate a wide variety of design methods that are based on the morphological chart.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

scholarly journals Genetic Algorithm: Reviews, Implementations, and Applications

2020 ◽  
Vol 10 (6) ◽  
pp. 57
Author(s):  
Tanweer Alam ◽  
Shamimul Qamar ◽  
Amit Dixit ◽  
Mohamed Benaida
Keyword(s):  
Genetic Algorithm ◽  
Natural Selection ◽  
Behavioral Science ◽  
Random Search ◽  
Statistical Investigation ◽  
Adaptive Technology ◽  
Mutation Operators ◽  
Engineering Pedagogy ◽  
Crossover And Mutation ◽  
Investigation Process

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

scholarly journals A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Alberto Pajares ◽  
Xavier Blasco ◽  
Juan M. Herrero ◽  
Gilberto Reynoso-Meza
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Maker ◽  
Pareto Front ◽  
Optimization Problem ◽  
Engineering Problem ◽  
Multiobjective Optimization Problem ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Pi Controllers

Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.

An Improved PBIL Algorithm for the Machine-Part Cell Formation

2010 ◽  
Vol 26-28 ◽  
pp. 498-501 ◽  
Author(s):  
Zhu Wang ◽  
Qing Bin Zhang ◽  
Yan Fang Ma ◽  
Jing Zhang ◽  
Yuan Liu
Keyword(s):  
Machine Part ◽  
Optimization Problem ◽  
Cell Formation ◽  
Past Research ◽  
Selection Strategy ◽  
Permutation Code ◽  
Combinational Optimization ◽  
Mutation Operators ◽  
Np Complete ◽  
Crossover And Mutation

The machine-part cell formation is a NP- complete combinational optimization problem. Past research has shown that although the genetic algorithm (GA) can get high quality solutions, special selection strategy, crossover and mutation operators as well as the parameters must be defined previously to solve the problem efficiently and flexibly. In this paper, an improved permutation code PBIL is adopted to solve the machine-part cell formation problem. Simulation results on five well known problems show that the PBIL can get satisfied solutions more simply and efficiently.

Transmission Network Expansion Planning under Improved Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 1458-1461
Author(s):  
Hong Fan ◽  
Yi Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Linear Optimization ◽  
Large Integer ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Search Capability

Improved genetic algorithm for solving the transmission network expansion planning is presented in the paper. The module which considered the investment costs of new transmission facilities. It is a large integer linear optimization problem. In this work we present improved genetic algorithm to find the solution of excellent quality. This method adopts integer parameter encoded style and has nonlinear crossover and mutation operators, owns strong global search capability. Tests are carried out using a Brazilian Southern System and the results show the good performance.

Adaptive Genetic Algorithm for Hybrid Flow-Shop Scheduling

2013 ◽  
Vol 753-755 ◽  
pp. 2925-2929
Author(s):  
Xiao Chun Zhu ◽  
Jian Feng Zhao ◽  
Mu Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Adaptive Genetic Algorithm ◽  
Shop Scheduling ◽  
Mutation Operators ◽  
Hybrid Flow Shop Scheduling ◽  
Encoding Method ◽  
Crossover And Mutation

This paper studies the scheduling problem of Hybrid Flow Shop (HFS) under the objective of minimizing makespan. The corresponding scheduling simulation system is developed in details, which employed a new encoding method so as to guarantee the validity of chromosomes and the convenience of calculation. The corresponding crossover and mutation operators are proposed for optimum sequencing. The simulation results show that the adaptive Genetic Algorithm (GA) is an effective and efficient method for solving HFS Problems.

scholarly journals Evaluation of efficiency and reliability of airport processes using simulation tools

2021 ◽  
Vol 23 (4) ◽  
pp. 659-669
Author(s):  
Paweł Gołda ◽  
Tomasz Zawisza ◽  
Mariusz Izdebski
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Simulation Tool ◽  
Simulation Tools ◽  
Aircraft Landing ◽  
Mutation Operators ◽  
Decision Making Model ◽  
Crossover And Mutation ◽  
Efficiency And Reliability

The purpose of this paper is to evaluate the efficiency of airport processes using simulation tools. A critical review of selected scientific studies relating to the performance of airport processes with respect to reliability, particularly within the apron, has been undertaken. The developed decision-making model evaluates the efficiency of airport processes in terms of minimizing penalties associated with aircraft landing before or after the scheduled landing time. The model takes into account, among other things, aircraft take-offs and landings and separation times between successive aircraft. In order to be able to verify the correctness of the decision-making model, a simulation tool was developed to support decision making in the implementation of airport operations based on a genetic algorithm. A novel development of the structure of a genetic algorithm as well as crossover and mutation operators adapted to the determination of aircraft movement routes on the apron is presented. The developed simulation tool was verified on real input data.

scholarly journals Optimized Graph cut Color Image Segmentation using Genetic Algorithm with Weighted Constraints (OGcut)

2019 ◽  
Vol 8 (4S2) ◽  
pp. 583-587
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Color Image ◽  
Graph Cut ◽  
Input Image ◽  
Color Image Segmentation ◽  
Weighted Constraints ◽  
Mutation Operators ◽  
Cut Method ◽  
Crossover And Mutation

This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.

Evolutionary Design of Planar Kinematic Chains

1998 ◽  
Author(s):  
David R. Nielsen ◽  
Kazem Kazerounian
Keyword(s):  
Genetic Algorithm ◽  
Kinematic Chain ◽  
Type Synthesis ◽  
Kinematic Chains ◽  
Design Flexibility ◽  
Novel Approach ◽  
Chain Link ◽  
Synthesis Of Mechanisms ◽  
New Ideas ◽  
Crossover And Mutation

Abstract A procedure is developed to optimize planar mechanism type. A Genetic Algorithm is used to cycle populations of kinematic chain link adjacency matrices, through selection, crossover, and mutation. During this optimization, fit kinematic chains survive while unfit kinematic chains do not. Upon convergence, synthesized kinematic chains of high fitness remain. This technique was lead to be called the Genetic Algorithm for Type Synthesis (GATS). GATS introduces four new ideas for the type synthesis of mechanisms. First, it does not permute all possible kinematic chains. It searches for the best kinematic chains depending on a designer’s specifications. Second, larger size mechanisms can be generated because of the genetic algorithm’s evolutionary naturalness. Third, a novel approach was applied to genetic algorithms to allow the encodings to mutate in size. This allowed for addition or elimination of links in kinematic chains during evolution. Forth, a new property was deduced from mechanism topography that describes the mechanism design flexibility.

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