scholarly journals Single-stage to orbit ascent trajectory optimisation with reliable evolutionary initial guess

2021 ◽  
Author(s):  
Francesco Marchetti ◽  
Edmondo Minisci ◽  
Annalisa Riccardi
Keyword(s):  
Differential Evolution ◽  
Trajectory Optimization ◽  
Differential Evolution Algorithm ◽  
Initial Guess ◽  
Single Stage ◽  
Multiple Shooting ◽  
Multi Objective ◽  
Continuity Properties ◽  
Estimation Of Distribution ◽  
Evolution Algorithm

AbstractIn this paper, the ascent trajectory optimization of a lifting body Single-Stage To Orbit (SSTO) reusable launch vehicle is investigated. The work is carried out using a Direct Multiple Shooting method to solve the Optimal Control problem. The crucial initialisation of the optimisation process is performed by using a combination of two evolutionary algorithms, namely a Multi-Objective Parzen-based Estimation of Distribution (MOPED) algorithm and a Multi-Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA). Multi-Objective Parzen-based Estimation of Distribution (MOPED) belongs to the class of Estimation of Distribution Algorithms (EDAs) and it is used in the first phase of the initial guess research to explore the search space, then Multi-Population Adaptive Inflationary Differential Evolution Algorithm (MP-AIDEA) is used to refine the obtained results, and better fulfill the imposed constraints. The initial guesses obtained with this evolutionary framework were tested on different multiple shooting configurations. The importance of the continuity properties of the employed mathematical models was also quantitatively addressed.

Distance-based immune generalised differential evolution algorithm for dynamic multi-objective optimisation

2021 ◽  
Vol 18 (2) ◽  
pp. 69
Author(s):  
María Guadalupe Martínez Peñaloza ◽  
Efrén Mezura Montes ◽  
Alicia Morales Reyes ◽  
Hernán E. Aguirre
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Multi Objective ◽  
Evolution Algorithm

A Differential Evolution Algorithm for Multi-objective Sparse Reconstruction

2021 ◽  
Vol 01 ◽  
Author(s):  
Xiaopei Zhu ◽  
Li Yan ◽  
Boyang Qu ◽  
Pengwei Wen ◽  
Zhao Li
Keyword(s):  
Differential Evolution ◽  
Signal Reconstruction ◽  
Differential Evolution Algorithm ◽  
Magnetic Resonance Images ◽  
Sparse Signal ◽  
Sparse Reconstruction ◽  
Reconstruction Problem ◽  
Sparse Signal Reconstruction ◽  
Multi Objective ◽  
Evolution Algorithm

Aims: This paper proposes a differential evolution algorithm to solve the multi-objective sparse reconstruction problem (DEMOSR). Background: The traditional method is to introduce the regularization coefficient and solve this problem through a regularization framework. But in fact, the sparse reconstruction problem can be regarded as a multi-objective optimization problem about sparsity and measurement error (two contradictory objectives). Objective: A differential evolution algorithm to solve multi-objective sparse reconstruction problem (DEMOSR) in sparse signal reconstruction and the practical application. Methods: First of all, new individuals are generated through tournament selection mechanism and differential evolution. Secondly, the iterative half thresholding algorithm is used for local search to increase the sparsity of the solution. To increase the diversity of solutions, a polynomial mutation strategy is introduced. Results: In sparse signal reconstruction, the performance of DEMOSR is better than MOEA/D-ihalf and StEMO. In addition, it can verify the effectiveness of DEMOSR in practical applications for sparse reconstruction of magnetic resonance images. Conclusions: According to the experimental results of DEMOSR in sparse signal reconstruction and the practical application of reconstructing magnetic resonance images, it can be proved that DEMOSR is effective in sparse signal and image reconstruction.

Phylogenetic Differential Evolution

2014 ◽  
pp. 22-40 ◽  
Author(s):  
Vinícius Veloso de Melo ◽  
Danilo Vasconcellos Vargas ◽  
Marcio Kassouf Crocomo
Keyword(s):  
Differential Evolution ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Building Blocks ◽  
Large Scale Systems ◽  
Estimation Of Distribution ◽  
Evolution Algorithm ◽  
A New Technique ◽  
Distribution Algorithms ◽  
Binary Problems

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to generate metavariables. The second one is used to find the best instance of each metavariable. In contrast to existing EDAs that identify the related variables at each iteration, the presented technique finds the related variables only once at the beginning of the algorithm, and not through the generations. This paper shows that the proposed technique is more efficient than the well known EDA called Extended Compact Genetic Algorithm (ECGA), especially for large-scale systems which are commonly found in real world problems.

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