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.

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

Planning of Component Placement/Insertion Sequence and Feeder Setup in PCB Assembly Using Genetic Algorithm

1993 ◽  
Vol 115 (4) ◽  
pp. 424-432 ◽  
Author(s):  
M. C. Leu ◽  
H. Wong ◽  
Z. Ji
Keyword(s):  
Genetic Algorithm ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Assembly Planning ◽  
Genetic Operators ◽  
Printed Circuit Board Assembly ◽  
Component Placement ◽  
Pcb Assembly ◽  
Circuit Board Assembly ◽  
Genetic Algorithm Approach

A new application of the genetic algorithm approach is introduced to solve printed circuit board assembly planning problems. The developed genetic algorithm finds the sequence of component placement/insertion and the arrangement of feeders simultaneously, for achieving the shortest assembly time, for three main types of assembly machines. The algorithm uses links (parents) to represent possible solutions and it applies genetic operators to generate new links (offspring) in an iterative procedure to obtain nearly optimal solutions. Examples are provided to illustrate solutions generated by the algorithm.

scholarly journals Exploring human genome feature for improving genetic algorithm performance

2019 ◽  
Vol 270 ◽  
pp. 03001
Author(s):  
Febri Zukhruf ◽  
Irma Susan Kurnia ◽  
Russ Bona Frazila ◽  
Gaga Irawan Nugraha ◽  
Mas Rizky A.A Syamsunarno
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Transportation Network ◽  
Network Design Problem ◽  
Darwinian Evolution ◽  
Genetic Operators ◽  
Algorithm Performance ◽  
Genome Feature ◽  
Hard Problems ◽  
Np Hard Problems

Genetic algorithm (i.e., GA) has longtermly obtained an extensive recognition for solving the optimization problem. Its pipelines process, which involves several operations, has been applied in many NP-hard problems, including the transportation network design problem (i.e., TNDP). As part of evolutionary computation methods, GA is inspired by Darwinian evolution, which is relied on the genetic operators (i.e., recombination, and mutation). On other side, the considerably achievement has been acquired by the genome researches, which offers an opportunity to deeply explore the recombination and mutation processes. This paper then presents variants of GA, which are inspired by the recent genome evidence of genetic operators. This exploration expectantly extends the benefit of evolution-based algorithm, which has been shown by the previous finding of GA. For examining the performance of proposed GA, the numerical experiment is involved for solving the TNDP. The performance comparisons show that the variation of crossover rate within a certain group of population provide better result than the standard GA.

Genetic Algorithm Approach for Optimization of FRP Laminated Cylindrical Shells Under Constraint

1999 ◽  
Author(s):  
Yoshiki Ohta
Keyword(s):  
Genetic Algorithm ◽  
Cylindrical Shells ◽  
Laminated Composite ◽  
Frequency Equation ◽  
Genetic Operators ◽  
Fundamental Frequencies ◽  
Design Variables ◽  
Structural Applications ◽  
Genetic Algorithm Approach ◽  
Laminated Cylindrical Shells

Abstract Fiber Reinforced Plastic (FRP) materials have been increasingly used in many structural applications of space shuttles, airplanes and automobiles, and the structural optimization of FRP laminated composite shells has been studied for stiffer structural design by many researchers. This paper studies the maximization of fundamental frequencies of FRP laminated cylindrical shells under stiffness constraint by using Genetic Algorithm (GA). For this purpose, the frequency equation for simply-supported shells with symmetrically balanced stacking sequence is derived analytically based on Classical Lamination Theory. In optimization the fiber angles and the thickness ratio of each FRP ply, which have continuous real values, are taken as design variables, and fundamental frequency of the shell is maximized under in-plane stiffness constraints. In numerical experiments, extensive numerical calculations are carried out to determine better genetic operators that would be suitable for FRP laminates design, and genetic parameters are tuned for better reliabilities and lower computational costs in the present GA. Optimal design solutions for various laminated cylindrical shells are obtained and then the applicability of the GA to the maximization of frequencies of the shells is studied from numerical results obtained.

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 Modelling and optimization of computer network traffic controllers

2005 ◽  
Vol 2005 (6) ◽  
pp. 617-640 ◽  
Author(s):  
N. U. Ahmed ◽  
Bo Li ◽  
Luis Orozco-Barbosa
Keyword(s):  
Genetic Algorithm ◽  
Network Traffic ◽  
Computer Network ◽  
Formal Model ◽  
Computation Time ◽  
Wide Range ◽  
Token Bucket ◽  
Speed Up ◽  
Genetic Algorithm Approach ◽  
Time Required

During the past years, there has been increasing interest in the design and development of network traffic controllers capable of ensuring the QoS requirements of a wide range of applications. In this paper, we construct a dynamic model for the token-bucket algorithm: a traffic controller widely used in various QoS-aware protocol architectures. Based on our previous work, we use a system approach to develop a formal model of the traffic controller. This model serves as a basis to formally specify and evaluate the operation of the token-bucket algorithm. Then we develop an optimization algorithm based on a dynamic programming and genetic algorithm approach. We conduct an extensive campaign of numerical experiments allowing us to gain insight on the operation of the controller and evaluate the benefits of using a genetic algorithm approach to speed up the optimization process. Our results show that the use of the genetic algorithm proves particularly useful in reducing the computation time required to optimize the operation of a system consisting of multiple token-bucket-regulated sources.

Genetic Operators for Catalog Design

1995 ◽  
Author(s):  
Susan E. Carlson ◽  
Todd A. Pegg
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Genetic Information ◽  
Basic System ◽  
System Configuration ◽  
Genetic Operators ◽  
Catalog Design ◽  
Genetic Algorithm Approach ◽  
Subsequent Selection ◽  
Selection Of

Abstract Catalog design is the process of forming functional systems by assembling selected components from manufacturers’ catalogs. Generally, the engineer performs this design process in two steps: the selection of a basic system configuration, and the subsequent selection of specific components for that configuration. A genetic algorithm approach to catalog design will allow the integration of these two steps. This paper focuses on the four components of a genetic algorithm that must be developed for application to the catalog design problem. These operators include: a genetic representation of the design that includes its component parts and their connectivity, an initialization process for generating an initial set of designs, and a method for the exchange of the designs’ genetic information.

GASBOR: A GENETIC ALGORITHM APPROACH FOR SOLVING THE SWITCHBOX ROUTING PROBLEM

1996 ◽  
Vol 06 (04) ◽  
pp. 359-373 ◽  
Author(s):  
JENS LIENIG ◽  
K. THULASIRAMAN
Keyword(s):  
Genetic Algorithm ◽  
Integrated Circuits ◽  
Design Process ◽  
Three Dimensional ◽  
Genetic Operators ◽  
Dimensional Representation ◽  
Routing Problem ◽  
Three Dimensional Representation ◽  
Genetic Algorithm Approach ◽  
Better Than

A new genetic algorithm for switchbox routing in the physical design process of integrated circuits is presented. Our algorithm, called GASBOR (Genetic Algorithm for SwitchBOx Routing), is based on a three-dimensional representation of the switchbox and problem-specific genetic operators. The performance of the algorithm is tested on different benchmarks and it is shown that the results obtained using the proposed algorithm are either qualitatively similar to or better than the best published 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.

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