☆ 4.7 Article

Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design

SWARM AND EVOLUTIONARY COMPUTATION (2023)

Related references

Note: Only part of the references are listed.

Article Computer Science, Information Systems

An improved differential evolution by hybridizing with estimation-of-distribution algorithm

Yintong Li et al.

Summary: In this study, an improved differential evolution algorithm named IDE-EDA is proposed by hybridizing the estimation-of-distribution algorithm. The cooperative evolutionary framework combines LSHADE-RSP with EDA to enhance the exploitation capability. A new control parameter is introduced to balance exploitation and exploration, and a greed strategy is used to retain high-quality solutions. Comparison with state-of-the-art variants demonstrates the efficiency of IDE-EDA.

INFORMATION SCIENCES (2023)

Add to Collection

Article Engineering, Aerospace

Deep-space trajectory optimizations using differential evolution with self-learning

Jin Haeng Choi et al.

Summary: This paper presents a spacecraft trajectory optimization method based on self-adaptive/self-learning differential evolution (DE) algorithm, which effectively solves optimization problems in deep-space missions requiring multiple gravity-assists. The proposed algorithm reduces the dependence on predetermined mutation strategy and control parameters, and adaptively optimizes the strategy by reflecting experiences in previous evolution steps. Furthermore, the algorithm addresses the issue of converging to a local optimum through a re-initialization technique, and has achieved good performance in practice.

ACTA ASTRONAUTICA (2022)

Add to Collection

Article Automation & Control Systems

A differential evolution algorithm with the guided movement for population and its application to interplanetary transfer trajectory design

Mingcheng Zuo et al.

Summary: This paper proposes a powerful differential evolution algorithm called G_DE to tackle the global optimization problem in interplanetary transfer trajectory design. With a two-stage evolutionary process and four guidance strategies, G_DE is able to find the current known best solutions and demonstrates comparable performance to other differential evolution algorithms in experimental tests.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2022)

Add to Collection

Article Computer Science, Information Systems

A novel adaptive L-SHADE algorithm and its application in UAV swarm resource configuration problem

Yintong Li et al.

Summary: This study proposes a novel adaptive L-SHADE algorithm, named AL-SHADE, to further improve the performance of L-SHADE in the field of differential evolution (DE). By modifying the mutation process and introducing a selection strategy with an adaptation scheme, AL-SHADE outperforms other competitors in terms of convergence efficiency and accuracy. Furthermore, AL-SHADE demonstrates promising performance in solving constrained optimization problems.

INFORMATION SCIENCES (2022)

Add to Collection

Article Computer Science, Artificial Intelligence

Differential evolution with mixed mutation strategy based on deep reinforcement learning

Zhiping Tan et al.

Summary: This study proposes a mixed mutation strategy DE algorithm DEDQN, which utilizes deep reinforcement learning for adaptive selection of mutation strategies. The experimental results demonstrate the competitive performance of the proposed algorithm.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Energy & Fuels

Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models

Zhenzhen Hu et al.

Summary: A new algorithm that combines differential evolution algorithm with reinforcement learning is proposed in this paper, which adjusts parameter values through reinforcement learning in the iterative process to obtain the most suitable algorithm parameters for the environment model.

ENERGY REPORTS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications

Majdi Radaideh et al.

Summary: In this study, a rule-based RL method is proposed to guide evolutionary algorithms in constrained optimization, showing significant improvement in continuous optimization over standalone algorithms. RL-guided EA outperforms standalone algorithms by more than 10 times in exploration capabilities and computational efficiency, indicating the effectiveness of RL in focusing the search space in areas where expert knowledge has merit.

KNOWLEDGE-BASED SYSTEMS (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Q-Learning-based parameter control in differential evolution for structural optimization

Thanh N. Huynh et al.

Summary: This paper introduces the Q-learning Differential Evolution (qlDE) algorithm for structural optimization, which integrates the reinforcement learning Q-learning model into the DE algorithm to adaptively adjust control parameters and enhance performance. The qlDE algorithm automatically controls the balance of exploration and exploitation to improve performance in structural weight minimization tasks.

APPLIED SOFT COMPUTING (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

Adaptive multi-population inflationary differential evolution

Marilena Di Carlo et al.

SOFT COMPUTING (2020)

Add to Collection

Article Computer Science, Information Systems

A local search algorithm with reinforcement learning based repair procedure for minimum weight independent dominating set

Yiyuan Wang et al.

INFORMATION SCIENCES (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy

Ying Huang et al.

APPLIED SOFT COMPUTING (2020)

Add to Collection

Article Computer Science, Artificial Intelligence

Differential evolution based on reinforcement learning with fitness ranking for solving multimodal multiobjective problems

Zhihui Li et al.

SWARM AND EVOLUTIONARY COMPUTATION (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Distance based parameter adaptation for Success-History based Differential Evolution

Adam Viktorin et al.

SWARM AND EVOLUTIONARY COMPUTATION (2019)

Add to Collection

Article Engineering, Aerospace

Enhanced Hybrid Differential Evolution for Earth-Moon Low-Energy Transfer Trajectory Optimization

Yanyun Zhang et al.

INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING (2018)

Add to Collection

Article Computer Science, Information Systems

L-SHADE optimization algorithms with population-wide inertia

Adam P. Piotrowski

INFORMATION SCIENCES (2018)

Add to Collection

Article Computer Science, Artificial Intelligence

An Inflationary Differential Evolution Algorithm for Space Trajectory Optimization

Massimiliano Vasile et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2011)

Add to Collection

Article Computer Science, Artificial Intelligence

JADE: Adaptive Differential Evolution With Optional External Archive

Jingqiao Zhang et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2009)

Add to Collection

Article Computer Science, Artificial Intelligence

KEEL: a software tool to assess evolutionary algorithms for data mining problems

J. Alcala-Fdez et al.

SOFT COMPUTING (2009)

Add to Collection

Review Neurosciences

Reinforcement learning: The Good, The Bad and The Ugly

Peter Dayana et al.

CURRENT OPINION IN NEUROBIOLOGY (2008)

Add to Collection

Article Computer Science, Information Systems

Adaptive evolutionary programming based on reinforcement learning

Huaxiang Zhang et al.

INFORMATION SCIENCES (2008)

Add to Collection

© Peeref 2019-2024. All rights reserved.