Suchergebnisse - "Hybrid multi-objective evolutionary algorithms"

Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

Autoren: Ou Ding Keqin Li Rong Qu et al.

Quelle: Applied Soft Computing. 68:268-282

Schlagwörter: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 7. Clean energy

Dateibeschreibung: PDF

Zugangs-URL: https://nottingham-repository.worktribe.com/preview/948614/ASC2018.pdf
https://nottingham-repository.worktribe.com/file/948602/1/ASC2018.pdf

Hybrid Multi-Objective Evolutionary Algorithms with Collective Intelligence

Autoren: Luis Martí Nayat Sanchez-Pi Ana Cristina Bicharra Garcia et al.

Quelle: Evolutionary Multi-Objective System Design ISBN: 9781315366845

Zugangs-URL: https://www.taylorfrancis.com/books/e/9781315366845/chapters/10.1201/9781315366845-3
https://content.taylorfrancis.com/books/download?dac=C2016-0-95896-1&isbn=9781315366845&doi=10.1201/9781315366845-3&format=pdf

Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration

Autoren: Marcel van Herk Kleopatra Pirpinia Peter A. N. Bosman et al.

Quelle: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. :1255-1262

Schlagwörter: 2. Zero hunger, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ir.cwi.nl/pub/23897/23897A.pdf
https://ir.cwi.nl/pub/23897
https://ir.cwi.nl/pub/23897/23897A.pdf
https://dl.acm.org/doi/10.1145/2739480.2754719
https://core.ac.uk/display/92561871
https://dblp.uni-trier.de/db/conf/gecco/gecco2015.html#PirpiniaASHB15
https://www.narcis.nl/publication/RecordID/oai%3Acwi.nl%3A23897

Risk conscious solution of planning problems under uncertainty by hybrid multi-objective evolutionary algorithms

Autoren: Thomas Tometzki Sebastian Engell

Quelle: Computers & Chemical Engineering. 35:2521-2539

Schlagwörter: 0211 other engineering and technologies, 02 engineering and technology, 0204 chemical engineering

Zugangs-URL: https://dblp.uni-trier.de/db/journals/cce/cce35.html#TometzkiE11
http://www.sciencedirect.com/science/article/pii/S0098135411000548
https://www.sciencedirect.com/science/article/pii/S0098135411000548
https://doi.org/10.1016/j.compchemeng.2011.01.044

Hybrid multi-objective evolutionary algorithms for the residential demand side management with thermal and electrical loads

Autoren: Garroussi, Zineb Talbi, El-Ghazali Ellaia, Rachid et al.

Weitere Verfasser: Garroussi, Zineb Talbi, El-Ghazali Ellaia, Rachid et al.

Schlagwörter: [INFO] Computer Science [cs], [INFO.INFO-RO] Computer Science [cs]/Operations Research [math.OC]

Zugangs-URL: https://inria.hal.science/hal-01654818v1

Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration

Autoren: Silva, S. Esparcia-Alcázar, A.I. Pirpinia, K. (Kleopatra) et al.

Index Begriffe: info:eu-repo/semantics/conferenceObject

URL: http://ir.cwi.nl/pub/23897

Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization.

Autoren: Xu, Ying Ding, Ou Qu, Rong et al.

Quelle: Applied Soft Computing. Jul2018, Vol. 68, p268-282. 15p.

Schlagwörter: Wireless sensor networks, Evolutionary algorithms, Internet service providers, Computer simulation, Multidisciplinary design optimization

Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction.

Autoren: Smith, Christopher Yaochu Jin

Quelle: Neurocomputing. Nov2014, Vol. 143, p302-311. 10p.

Schlagwörter: *ARTIFICIAL neural networks, *TIME series analysis, *PREDICTION theory, *PERFORMANCE evaluation, *ALGORITHMS, *ERROR analysis in mathematics

Analysis of the Multi-Objective Control Sequence Optimization Problem in Bivariate Fertilizer Applicators.

Autoren: Zhang, Jiqin Zhuang, Qibin Liu, Gang

Quelle: Symmetry (20738994); Jun2025, Vol. 17 Issue 6, p926, 20p

Schlagwörter: MULTI-objective optimization, EVOLUTIONARY algorithms, PRECISION farming, FERTILIZERS, UNIFORMITY

An efficient multi-objective parrot optimizer for global and engineering optimization problems.

Autoren: Saad, Mohammed R. Emam, Marwa M. Houssein, Essam H.

Quelle: Scientific Reports; 2/11/2025, Vol. 15 Issue 1, p1-33, 33p

Schlagwörter: METAHEURISTIC algorithms, GREY Wolf Optimizer algorithm, MULTI-objective optimization, PARTICLE swarm optimization, COMPUTATIONAL mathematics

Transit search algorithm based on oscillation exploitation factor and Roche limit for wireless sensor network deployment optimization.

Autoren: Xing, Yu-Xuan Wang, Jie-Sheng Zhang, Si-Wen et al.

Quelle: Artificial Intelligence Review; Jan2025, Vol. 58 Issue 1, p1-51, 51p

A Novel Sensor Deployment Strategy Based on Probabilistic Perception for Industrial Wireless Sensor Network.

Autoren: Liu, Xiaokai Xu, Fangmin Ning, Lina et al.

Quelle: Electronics (2079-9292); Dec2024, Vol. 13 Issue 24, p4952, 18p

Schlagwörter: SENSOR placement, WIRELESS sensor networks, SENSOR networks, NETWORK performance, INTELLIGENT networks

Wireless sensor network coverage of improved sea lion algorithm.

Autoren: Kagi, Swati Shivakumar Mallapur, Sujata Veeresh

Quelle: International Journal of Communication Systems; Dec2024, Vol. 37 Issue 18, p1-17, 17p

Schlagwörter: SENSOR placement, SEA lions, METAHEURISTIC algorithms, PROBLEM solving, RESOURCE management

A comprehensive survey on optimization techniques for efficient cluster based routing in WSN.

Autoren: Karpurasundharapondian, P. Selvi, M.

Quelle: Peer-to-Peer Networking & Applications; Sep2024, Vol. 17 Issue 5, p3080-3093, 14p

Schlagwörter: OPTIMIZATION algorithms, WIRELESS sensor networks, TRAFFIC monitoring, ENERGY consumption, MATHEMATICAL optimization, MULTICASTING (Computer networks)

Efficient fog node placement using nature-inspired metaheuristic for IoT applications.

Autoren: Naouri, Abdenacer Nouri, Nabil Abdelkader Khelloufi, Amar et al.

Quelle: Cluster Computing; Sep2024, Vol. 27 Issue 6, p8225-8241, 17p

Schlagwörter: INTELLIGENT networks, NETWORK performance, QUALITY of service, COMPUTATIONAL complexity, ALGORITHMS

An improved tuna swarm optimization algorithm based on behavior evaluation for wireless sensor network coverage optimization.

Autoren: Chang, Yu He, Dengxu Qu, Liangdong

Quelle: Telecommunication Systems; Aug2024, Vol. 86 Issue 4, p829-851, 23p

Schlagwörter: OPTIMIZATION algorithms, BEHAVIORAL assessment, SIMPLEX algorithm, SWARM intelligence, TUNA, PARTICLE swarm optimization, WIRELESS sensor networks, SENSOR networks

Coverage Prediction for Target Coverage in WSN Using Machine Learning Approaches.

Autoren: Chaturvedi, Pooja Daniel, A. K. Narayan, Vipul

Quelle: Wireless Personal Communications; Jul2024, Vol. 137 Issue 2, p931-950, 20p

Schlagwörter: WIRELESS sensor networks, RANDOM forest algorithms, COMPUTATIONAL complexity, TRUST, PREDICTION models, MACHINE learning, MATHEMATICAL programming

Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer.

Autoren: Kong, Weibin Cai, Xin Du, Yi et al.

Quelle: Electronics Letters (Wiley-Blackwell); Jun2024, Vol. 60 Issue 11, p1-4, 4p

Schlagwörter: WIRELESS sensor networks, GOLD, SEARCH algorithms, LEARNING strategies, ENERGY consumption

A Synergistic MOEA Algorithm with GANs for Complex Data Analysis.

Autoren: Qian, Weihua Xu, Hang Chen, Houjin et al.

Quelle: Mathematics (2227-7390); Jan2024, Vol. 12 Issue 2, p175, 30p

Schlagwörter: EVOLUTIONARY algorithms, GENERATIVE adversarial networks, ALGORITHMS, ENTROPY

Fuzzy‐based techniques for clustering in wireless sensor networks (WSNs): Recent advances, challenges, and future directions.

Autoren: Verma, Sandeep Bhatia, Sakshi Zeadally, Sherali et al.

Quelle: International Journal of Communication Systems; 11/10/2023, Vol. 36 Issue 16, p1-25, 25p

Schlagwörter: WIRELESS sensor networks, SENSOR networks, ENERGY consumption, FUZZY logic