scholarly journals A genetic algorithm for the routing and carrier selection problem

2012 ◽  
Vol 9 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Jozef Kratica ◽  
Tijana Kostic ◽  
Dusan Tosic ◽  
Djordje Dugosija ◽  
Vladimir Filipovic
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Numerical Experiments ◽  
Selection Problem ◽  
Evolutionary Approach ◽  
Data Sets ◽  
Genetic Operators ◽  
Encoding Scheme ◽  
Standard Data ◽  
Carrier Selection

In this paper we present new evolutionary approach for solving the Routing and Carrier Selection Problem (RCSP). New encoding scheme is implemented with appropriate objective function. This approach in most cases keeps the feasibility of individuals by using specific representation and modified genetic operators. The numerical experiments were carried out on the standard data sets known from the literature and results were successful comparing to two other recent heuristic for solving RCSP.

scholarly journals A new genetic representation for quadratic assignment problem

2011 ◽  
Vol 21 (2) ◽  
pp. 225-238 ◽  
Author(s):  
Jozef Kratica ◽  
Dusan Tosic ◽  
Vladimir Filipovic ◽  
Djordje Dugosija
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Numerical Experiments ◽  
Quadratic Assignment Problem ◽  
Quadratic Assignment ◽  
Data Sets ◽  
Optimal Solutions ◽  
Genetic Operators ◽  
Reasonable Time ◽  
Genetic Encoding

In this paper, we propose a new genetic encoding for well known Quadratic Assignment Problem (QAP). The new encoding schemes are implemented with appropriate objective function and modified genetic operators. The numerical experiments were carried out on the standard QAPLIB data sets known from the literature. The presented results show that in all cases proposed genetic algorithm reached known optimal solutions in reasonable time.

MIXED GENETIC ALGORITHM APPROACH FOR FUZZY CLASSIFIER DESIGN

2010 ◽  
Vol 09 (01) ◽  
pp. 49-67 ◽  
Author(s):  
D. DEVARAJ ◽  
P. GANESH KUMAR
Keyword(s):  
Genetic Algorithm ◽  
Data Sets ◽  
Membership Functions ◽  
Genetic Operators ◽  
Fuzzy Classifier ◽  
Classifier Design ◽  
Standard Data ◽  
Mutation Operators ◽  
Minimum Number ◽  
Mixed Genetic Algorithm

An important issue in the design of FRBS is the formation of fuzzy if-then rules and the membership functions. This paper presents a Mixed Genetic Algorithm (MGA) approach to obtain the optimal rule set and the membership function of the fuzzy classifier. While applying genetic algorithm for fuzzy classifier design, the membership functions are represented as real numbers and the fuzzy rules are represented as binary string. Modified forms of crossover and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence of GA and accuracy of the classifier. The performance of the proposed approach is evaluated through development of fuzzy classifier for seven standard data sets. From the simulation study it is found that the proposed algorithm produces a fuzzy classifier with minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.

Genetic Algorithm Approach for UAV Persistent Visitation Problem

2018 ◽  
Author(s):  
Alexander L. Von Moll ◽  
David W. Casbeer ◽  
Krishna Kalyanam ◽  
Satyanarayana G. Manyam
Keyword(s):  
Genetic Algorithm ◽  
Computation Time ◽  
Exact Method ◽  
Genetic Operators ◽  
Exact Methods ◽  
Optimal Length ◽  
Algorithm Performance ◽  
Encoding Scheme ◽  
Fixed Length ◽  
Genetic Algorithm Approach

We employ a genetic algorithm approach to solving the persistent visitation problem for UAVs. The objective is to minimize the maximum weighted revisit time over all the sites in a cyclicly repeating walk. In general, the optimal length of the walk is not known, so this method (like the exact methods) assume some fixed length. Exact methods for solving the problem have recently been put forth, however, in the absence of additional heuristics, the exact method scales poorly for problems with more than 10 sites or so. By using a genetic algorithm, performance and computation time can be traded off depending on the application. The main contributions are a novel chromosome encoding scheme and genetic operators for cyclic walks which may visit sites more than once. Examples show that the performance is comparable to exact methods with better scalability.

Widening Frequency Band of Uniform SPL Distribution Using Adaptive Genetic Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1040-1043
Author(s):  
Zhao Xin Huang ◽  
Sai Ma ◽  
Hui Wang
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Uniform Distribution ◽  
Frequency Band ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Directivity Pattern ◽  
Pressure Level ◽  
Adaptive Genetic Algorithm ◽  
Genetic Operators

Uniform sound pressure level (SPL) distribution of linear phased loudspeaker array is limited by frequency. This paper widens the applicable frequency band of uniform SPL distribution in a linear listening area. By using an improved adaptive genetic algorithm (which contains a novel objective function, modified genetic operators and parameter setups) to control the directivity pattern details accurately, uniform distribution of SPL on a linear listening line in a wider frequency is achieved. The simulation and experimental results show that the SPLs on the test listening line are basically uniform from 200Hz to 500Hz, which demonstrates that the improvement of adaptive genetic algorithm is effective.

Genetic Algorithm Solution for a Member Selection Problem of Supply Chain Network

2011 ◽  
Vol 204-210 ◽  
pp. 2140-2143
Author(s):  
Peng Yang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Objective Function ◽  
Selection Problem ◽  
Principal Agent ◽  
Supply Chain Network ◽  
Reasonable Solution ◽  
Member Selection

This paper studies a member selection problem of a supply chain network consisting of many different projects, such as supply, manufacturing, distribution, and logistics. It first takes those different projects as a whole one, considers the principal-agent relationships among these enterprises and establishes models using the nonanalytical objective function. It also proposes a genetic algorithm to generate a relatively reasonable solution and validates it with an example.

Variable Selection in Multiple Linear Regression Using a Genetic Algorithm

2016 ◽  
pp. 133-159 ◽  
Author(s):  
Javier Trejos ◽  
Mario A. Villalobos-Arias ◽  
Jose Luis Espinoza
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Least Squares ◽  
Fitness Function ◽  
Data Sets ◽  
Genetic Operators ◽  
Wheel Model ◽  
Crossover And Mutation

In this article it is studied the application of a genetic algorithm in the problem of variable selection for multiple linear regression, minimizing the least squares criterion. The algorithm is based on a chromosomic representation of variables that are considered in the least squares model. A binary chromosome indicates the presence (1) or absence (0) of a variable in the model. The fitness function is based on the adjusted square R, proportional to the fitness for chromosome selection in a roulette wheel model selection. Usual genetic operators, such as crossover and mutation are implemented. Comparisons are performed with benchmark data sets, obtaining satisfying and promising results.

An effective genetic algorithm for job shop scheduling

2000 ◽  
Vol 214 (4) ◽  
pp. 293-300 ◽  
Author(s):  
W Wang ◽  
P Brunn
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Gene Encoding ◽  
Encoding Scheme ◽  
Shop Scheduling ◽  
Sequencing And Scheduling

This paper presents an effective genetic algorithm (GA) for job shop sequencing and scheduling. A simple and universal gene encoding scheme for both single machine and multiple machine models and their corresponding genetic operators, selection, sequence-extracting crossover and neighbour-swap mutation are described in detail. A simple heuristic rule is adapted and embedded into the GA to avoid the production of unfeasible solutions. The results of computing experiments for a number of scheduling problems have demonstrated that the GA described in the paper is effective and efficient in terms of the quality of solution and the computing cost.

scholarly journals A Hybrid Intelligent Algorithm for Optimal Birandom Portfolio Selection Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Qi Li ◽  
Guo-Hua Cao ◽  
Dan Shan
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Portfolio Selection ◽  
Numerical Experiments ◽  
Intelligent Algorithm ◽  
Hybrid Intelligent Algorithm ◽  
Selection Problems

Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

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