scholarly journals Multiple Gravity Assist Spacecraft Trajectories Design Based on BFS and EP_DE Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mingcheng Zuo ◽  
Guangming Dai ◽  
Lei Peng ◽  
Maocai Wang ◽  
Jinlian Xiong
Keyword(s):  
Differential Evolution ◽  
Heuristic Algorithms ◽  
Differential Evolution Algorithm ◽  
Principal Component ◽  
Search Space ◽  
Optimal Decision ◽  
Gravity Assist ◽  
Spacecraft Trajectories ◽  
Evolution Algorithm ◽  
Decision Vector

The paper deals with the multiple gravity assist trajectories design. In order to improve the performance of the heuristic algorithms, such as differential evolution algorithm, in multiple gravity assist trajectories design optimization, a method combining BFS (breadth-first search) and EP_DE (differential evolution algorithm based on search space exploring and principal component analysis) is proposed. In this method, firstly find the possible multiple gravity assist planet sequences with pruning based BFS and use standard differential evolution algorithm to judge the possibility of all the possible trajectories. Then select the better ones from all the possible solutions. Finally, use EP_DE which will be introduced in this paper to find an optimal decision vector of spacecraft transfer time schedule (launch window and transfer duration) for each selected planet sequence. In this paper, several cases are presented to prove the efficiency of the method proposed.

Calibration Zone for the Parameters of the Differential Evolution Algorithm and Its Application to a Real Burst Location Problem

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
A. Pérez-González ◽  
A. Badillo-Olvera ◽  
O. Begovich ◽  
J. Ruíz-León
Keyword(s):  
Differential Evolution ◽  
Location Problem ◽  
Heuristic Algorithms ◽  
Differential Evolution Algorithm ◽  
Iterative Technique ◽  
Location Task ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Calibration Parameters

Numerical problems are usually solved using heuristic algorithms, due to their simplicity and easy understanding. Nevertheless, most of these methods have calibration parameters that do not count with selection premises oriented to obtain the best performance for the algorithm. This paper introduces an iterative technique that deals with this problem, searching for the calibration parameters that improve the Differential Evolution (DE) algorithm. The application of the proposed technique is illustrated on a real burst location problem in a pipeline prototype. The obtained results show the good performance of the methodology proposed for the burst location task, including the mapping of the calibration parameters that ameliorate the searching process.

P-lsGOF: A parallel learning-selection-based global optimization framework

2020 ◽  
Vol 39 (5) ◽  
pp. 7333-7361
Author(s):  
Mingcheng Zuo ◽  
Guangming Dai
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Principal Component ◽  
Parallel Learning ◽  
Optimization Framework ◽  
Engineering Design Problems ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

When optimizing complicated engineering design problems, the search spaces are usually extremely nonlinear, leading to the great difficulty of finding optima. To deal with this challenge, this paper introduces a parallel learning-selection-based global optimization framework (P-lsGOF), which can divide the global search space to numbers of sub-spaces along the variables learned from the principal component analysis. The core search algorithm, named memory-based adaptive differential evolution algorithm (MADE), is parallel implemented in all sub-spaces. MADE is an adaptive differential evolution algorithm with the selective memory supplement and shielding of successful control parameters. The efficiency of MADE on CEC2017 unconstrained problems and CEC2011 real-world problems is illustrated by comparing with recently published state-of-the-art variants of success-history based adaptative differential evolution algorithm with linear population size reduction (L-SHADE) The performance of P-lsGOF on CEC2011 problems shows that the optimized results by individually conducting MADE can be further improved.

EP_DE II: A significant algorithm to search the optimal solution for global optimization of multi-gravity assist trajectory

2017 ◽  
Vol 232 (12) ◽  
pp. 2326-2337
Author(s):  
Mingcheng Zuo ◽  
Guangming Dai ◽  
Lei Peng
Keyword(s):  
Principal Component Analysis ◽  
Search Algorithm ◽  
Selection Process ◽  
Differential Evolution Algorithm ◽  
Evolutionary Process ◽  
Principal Component ◽  
Search Space ◽  
Component Analysis ◽  
Parameter Distribution ◽  
Gravity Assist

The paper deals with the design of the optimal multiple gravity assist trajectories. An improved search algorithm named EP_DE II with search space exploration, principal component analysis, guarantee mechanisms, and processing methods of search space is proposed based on EP_DE. First, a parameter is employed to start retaining the boundary information of the whole population. When the number of generations in evolutionary process reaches to this parameter, distribution range of population will be retained for the later computation. The best solutions in each generation are also recorded until the end of EP_DE II. Then the principal component analysis was conducted to find a cutting dimension for local search space, and selection process of cutting points are directed by the stored information before. Finally, search process is performed in all partitions of the search space. Global search experiments concerned about Benchmark Cassini1 and fly-by sequence EVVEEJS are presented to prove the efficiency of EP_DE II algorithm, comparing with basic differential evolution algorithm and EP_DE algorithm.

Differential Evolution Algorithm with Space Reduction for Solving Large-Scale Global Optimization Problems

2017 ◽  
pp. 671-694
Author(s):  
Ahmed Fouad Ali ◽  
Nashwa Nageh Ahmed
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Search Space ◽  
Evolution Algorithm ◽  
Scale Spaces ◽  
Heuristics Algorithm ◽  
Scale Optimization

Differential evolution algorithm (DE) is one of the most applied meta-heuristics algorithm for solving global optimization problems. However, the contributions of applying DE for large-scale global optimization problems are still limited compared with those problems for low dimensions. In this chapter, a new differential evolution algorithm is proposed in order to solve large-scale optimization problems. The proposed algorithm is called differential evolution with space partitioning (DESP). In DESP algorithm, the search variables are divided into small groups of partitions. Each partition contains a certain number of variables and this partition is manipulated as a subspace in the search process. Searching a limited number of variables in each partition prevents the DESP algorithm from wandering in the search space especially in large-scale spaces. The proposed algorithm is investigated on 15 benchmark functions and compared against three variants DE algorithms. The results show that the proposed algorithm is a cheap algorithm and obtains good results in a reasonable time.

Application of Natured-Inspired Technique to Odia Handwritten Numeral Recognition

2018 ◽  
pp. 377-399 ◽  
Author(s):  
Puspalata Pujari ◽  
Babita Majhi
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Principal Component ◽  
Performance Comparison ◽  
Functional Link ◽  
Swarm Optimization ◽  
Handwritten Numeral Recognition ◽  
S Transform ◽  
Evolution Algorithm ◽  
Numeral Recognition

In this chapter an effort has been made to develop a hybrid system using functional link artificial neural network (FLANN) and differential evolution (DE) for effective recognition of Odia handwritten numerals. The S-transform (ST) is chosen for feature extraction from handwritten numerals and these are further reduced by using principal component analysis (PCA). After reduction of feature the reduced features are applied to FLANN model for recognition of each numeral. Further differential evolution algorithm (DE) is used for the optimization of weights of FLANN classifier. For performance comparison, genetic algorithm (GA) and particle swarm optimization (PSO) based FLANN models (FLANN_GA and FLANN_PSO) are also designed and simulated under similar condition. The efficiency of proposed DE based FLANN (FLANN_DE) method is assessed through simulation with standard dataset consisting of 4000 handwritten Odia numerals. The results of three models are compared and it is observed that the FLANN_DE model provides the best result as compared to other models.

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