scholarly journals Autonomous Planning of Multigravity-Assist Trajectories with Deep Space Maneuvers Using a Differential Evolution Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ossama Abdelkhalik
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Optimal Number ◽  
Deep Space ◽  
Biologically Inspired ◽  
Autonomous Planning ◽  
Design Variables ◽  
Planning Problems ◽  
Evolution Approach

The biologically inspired concept of hidden genes has been recently introduced in genetic algorithms to solve optimization problems where the number of design variables is variable. In multigravity-assist trajectories, the hidden genes genetic algorithms demonstrated success in searching for the optimal number of swing-bys and the optimal number of deep space maneuvers. Previous investigations in the literature for multigravity-assist trajectory planning problems show that the standard differential evolution is more effective than the standard genetic algorithms. This paper extends the concept of hidden genes to differential evolution. The hidden genes differential evolution is implemented in optimizing multigravity-assist space trajectories. Case studies are conducted, and comparisons to the hidden genes genetic algorithms are presented in this paper.

scholarly journals A Multi-Angle Hierarchical Differential Evolution Approach for Multimodal Optimization Problems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 178322-178335
Author(s):  
Zhao Hong ◽  
Zong-Gan Chen ◽  
Dong Liu ◽  
Zhi-Hui Zhan ◽  
Jun Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Multimodal Optimization ◽  
Evolution Approach

scholarly journals Representasi Matriks untuk Proses Crossover Pada Algoritma Genetika untuk Optimasi Travelling Salesman Problem

Matematika ◽  
2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Ismi Fadhillah ◽  
Yurika Permanasari ◽  
Erwin Harahap
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Matrix Representation ◽  
Travelling Salesman Problem ◽  
Optimal Number ◽  
Travelling Salesman ◽  
Combinatorial Optimization Problems ◽  
Fitness Value ◽  
The City

Abstrak. Travelling Salesman Problem (TSP) merupakan salah satu permasalahan optimasi kombinatorial yang biasa terjadi dalam kehidupan sehari-hari. Permasalahan TSP yaitu mengenai seseorang yang harus mengunjungi semua kota tepat satu kali dan kembali ke kota awal dengan jarak tempuh minimal. TSP dapat diselesaikan dengan menggunakan metode Algoritma Genetika. Dalam Algoritma Genetika, representasi matriks merupakan representasi kromosom yang menunjukan sebuah perjalanan. Jika dalam perjalanan tersebut melewati n kota maka akan dibentuk matriks n x n. Matriks elemen Mij dengan baris i dan kolom j dimana entry M(i,j) akan bernilai 1 jika dan hanya jika kota i dikunjungi sebelum kota j dalam satu perjalanan tersebut, selain itu M(i,j)=0. Crossover adalah mekanisme yang dimiliki algoritma genetika dengan menggabungkan dua kromosom sehingga menghasilkan anak kromosom yang mewarisi ciri-ciri dasar dari parent. Algoritma Genetika selain melibatkan populasi awal dalam proses optimasi juga membangkitkan populasi baru melalui proses crossover, sehingga dapat memberikan daftar variabel yang optimal bukan hanya solusi tunggal. Dari hasil proses crossover dalam contoh kasus TSP melewati 6 kota, terdapat 2 kromosom anak terbaik dengan nilai finess yang sama yaitu 0.014. Algoritma Genetika dapat berhenti pada generasi II karena berturut-turut mendapat nilai fitness tertinggi yang tidak berubahKata kunci : Travelling Salesman Program (TSP), Algoritma Genetika, Representasi Matriks, Proses Crossover Abstract. Travelling Salesman Problem (TSP) is one of combinatorial optimization problems in everyday life. TSP is about someone who had to visit all the cities exactly once and return to the initial city with minimal distances. TSP can be solved using Genetic Algorithms. In a Genetic Algorithm, a matrix representation represents chromosomes which indicates a journey. If in the course of the past n number of city will set up a matrix n x n. The matrix element Mij with row i and column j where entry M (i, j) will be equal to 1 if and only if the city i before the city j visited in one trip. In addition to the M (i, j) = 0. Crossover is a mechanism that is owned by the Genetic Algorithm to combine the two chromosomes to produce offspring inherited basic characteristics of the parent. Genetic Algorithms in addition to involve the population early in the optimization process will also generate new populations through the crossover process, so as to provide optimal number of variables is not just a single solution. From the results of the crossover process in the case of TSP passing through six cities, there are two the best offspring with the same finess value which is 0.014. Genetic Algorithms can be stopped on the second generation due to successive received the highest fitness value unchanged.Keywords: Travelling Salesman Program (TSP), Genetic Algorithm, Matrix Representation, Crossover Process

scholarly journals Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch

2019 ◽  
Vol 11 (3) ◽  
pp. 1-11
Author(s):  
Omar Andres Carmona Cortes ◽  
Josenildo Costa da Silva
Keyword(s):  
Genetic Algorithms ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Practical Applications ◽  
R Language ◽  
Mutation Operators ◽  
Design Variables ◽  
Study Case ◽  
Selection Mechanisms ◽  
Real Coded Genetic Algorithms

Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, and Alpine) are used as a study case. Further, four different crossover operators (simple, arithmetical, non-uniform arithmetical, and Linear), two selection mechanisms (roulette wheel and tournament), and two mutation operators (uniform and non-uniform) are shown. Results indicate that non-uniform mutation and tournament selection tend to present better outcomes.

scholarly journals Optimum Robot Design Based on Task Specifications Using Evolutionary Techniques and Kinematic, Dynamic, and Structural Constraints

2002 ◽  
Author(s):  
P. S. Shiakolas ◽  
D. Koladiya ◽  
J. Kebrle
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Cross Sections ◽  
Evolutionary Optimization ◽  
Robot Design ◽  
Structural Constraints ◽  
Link Length ◽  
Cross Sectional ◽  
Physical Constraints ◽  
Design Variables

In this paper, we discuss optimum robot design based on task specifications using evolutionary optimization approaches. The three evolutionary optimization approaches employed are Simple Genetic Algorithms, Genetic Algorithms with elitism, and Differential Evolution. These approaches were used for the optimum design of SCARA and articulated type manipulators. The objective function minimizes the torque required for the motion subject to deflection and physical constraints with the design variables being the physical characteristics of link (length and cross sectional area parameters). In this work, we experimented links with various cross sections. The main findings of this research are that the differential evolution converges quickly, requires significantly less number of iterations and achieves better results.

Evolving Hidden Genes in Genetic Algorithms for Systems Architecture Optimization

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Ossama Abdelkhalik ◽  
Shadi Darani
Keyword(s):  
Genetic Algorithms ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Mathematical Optimization ◽  
Space Mission ◽  
Systems Architecture ◽  
Design Variables ◽  
Interplanetary Trajectory ◽  
Second Category ◽  
Architecture Optimization

The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions.

Differential Evolution Applied to the Design of a Three-Dimensional Vehicular Structure

2007 ◽  
Author(s):  
Felipe Antonio Chegury Viana ◽  
Fernando Ce´sar Gama de Oliveira ◽  
Jose Antonio Ferreria Borges ◽  
Valder Steffen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Three Dimensional ◽  
Differential Evolution Algorithm ◽  
Basic Algorithm ◽  
Present Contribution ◽  
Finite Element Software ◽  
Design Variables ◽  
Numerical Noise ◽  
Discrete Design Variables

The purpose of this paper is to demonstrate the application of Differential Evolution to a realistic design optimization test problem. The present contribution regards the improvements implemented to the original basic algorithm as well as the application of a new algorithm for dealing with the unique challenges associated with real world optimization problems. The selected example is a three-dimensional vehicular structure optimization problem modeled using the commercial Finite Element software ANSYS® that has a combination of continuous and discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the Differential Evolution algorithm is able to find the optimum design for the proposed problem. The algorithm is robust in the sense that it is capable of dealing with the numerical noise involved in the modeling of the system and to manipulate discrete design variables, accordingly.

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