Self-learning genetic algorithm

Костенко, В. А.; Фролов, А. В.

doi:10.1134/s1064230715040103

Cited by 16 publications

(7 citation statements)

References 4 publications

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“…The GA is an intelligence algorithm which is widely used in many fields [17][18][19]. Taking the maximum value of the remaining fuel weight (max m remain ) as the optimization index, the transfer optimization model of a single OSV serving multiple satellites is established based on the GA.…”

Section: Optimization Modelmentioning

confidence: 99%

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Xing-long

Liu

et al. 2021

Modelling and Simulation in Engineering

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On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.

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Section: Optimization Modelmentioning

confidence: 99%

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Xing-long

Liu

et al. 2021

Modelling and Simulation in Engineering

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“…One way to solve the MOTO problem is to use the principle of "pareto-optimal" [93][94][95]. A pareto-optimal solution is optimal in the sense that no other solutions are superior (better) to it in the current searching space when all objectives are considered [96,97]. Since it is usually Improved nondominated sorting genetic algorithm II (I-NSGA-II) [75] Nondominated sorting genetic algorithm III (NSGA-III) [76] Multi-objective evolutionary algorithm Based on decomposition (MOEA/D) [77] Multi-objective particle swarm optimization (MOPSO) [78] Multi-objective adaptive particle swarm optimization (MOAPSO) [79] Multi-objective adaptive gradient particle swarm optimization (MOAGPSO) [80] Multi-objective artificial bee colony (MOABC) [81] Niched pareto genetic algorithm (NPGA) [82] Strength pareto particle swarm optimization (SPPSO) [83] Adaptive differential evolution and modified game theory (ADEMGT) [84]…”

Section: Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

A review of optimization techniques in spacecraft flight trajectory design

Chai

Tsourdos

Chai

et al. 2019

Progress in Aerospace Sciences

102

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For most atmospheric or exo-atmospheric spacecraft flight scenarios, a welldesigned trajectory is usually a key for stable flight and for improved guidance and control of the vehicle. Although extensive research work has been carried out on the design of spacecraft trajectories for different mission profiles and many effective tools were successfully developed for optimizing the flight path, it is only in the recent five years that there has been a growing interest in planning the flight trajectories with the consideration of multiple mission objectives and various model errors/uncertainties. It is worth noting that in many practical spacecraft guidance, navigation and control systems, multiple performance indices and different types of uncertainties must frequently be considered during the path planning phase. As a result, these requirements bring the development of multi-objective spacecraft trajectory optimization methods as well as stochastic spacecraft trajectory optimization algorithms. This paper aims to broadly review the state-of-the-art development in numerical multi-objective trajectory optimization algorithms and stochastic trajectory planning techniques for spacecraft flight operations. A brief description of the mathematical formulation of the problem is firstly introduced. Following that, various optimization methods that can be effective for solving spacecraft trajectory planning problems are reviewed, including the gradient-based methods, the convexification-based methods, and the evolutionary/metaheuristic methods. The multi-objective

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“…Genetic algorithm has the advantage of exploring the space of all possible subsets fairly well in a large but reasonable amount of time, which is much less than that required for the study of all possible subsets . Many variants of GA have been developed for combinatorial optimization, and numerous references have already shown that GA can be successfully used as a spectral variable selection technique …”

Section: Introductionmentioning

confidence: 99%

“…17,18 Genetic algorithm has the advantage of exploring the space of all possible subsets fairly well in a large but reasonable amount of time, which is much less than that required for the study of all possible subsets. [19][20][21] Many variants of GA have been developed for combinatorial optimization, [22][23][24][25][26] and numerous references have already shown that GA can be successfully used as a spectral variable selection technique. [27][28][29][30] A major problem with GA is the stochastic risk, since the probability of finding a suitable model is only by chance (ie, due to random correlations).…”

mentioning

confidence: 99%

Similar offspring voting genetic algorithm for spectral variable selection

Zheng

2017

Journal of Chemometrics

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This paper proposes a novel spectral variable selection approach named similar offspring voting genetic algorithm (sovGA). Unlike the traditional voting method (here called voting genetic algorithm) that attempts to overcome the stochastic risk of a genetic algorithm but may damage its optimal variable combination, sovGA aims to achieve a trade‐off between prediction precision and reliability of models. Genetic algorithm was repeated many times, and some effective similar offspring were obtained by a proposed algorithm; then, the spectral variables were selected by a voting mechanism. Two calibration methods, partial least square regression and multiple linear regression, were constructed to evaluate performance. The experimental results show that sovGA outperformed genetic algorithm, voting genetic algorithm, and the full spectrum situation in most cases.

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Self-learning genetic algorithm

Cited by 16 publications

References 4 publications

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

A review of optimization techniques in spacecraft flight trajectory design

Similar offspring voting genetic algorithm for spectral variable selection

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