Search Results - addition _ different (evolutionary OR (evolution OR solution)) algorithm

A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

A novel clustering based genetic algorithm for route optimization by Aibinu, Abiodun Musa, Salau, Habeeb Bello, Najeeb, Athaur Rahman, Nwohu, Mark Ndubuka, Akachukwu, Chichebe

“…Genetic Algorithm (GA), a random universal evolutionary search technique that imitates the principle of biological evolution has been applied in solving various problems in different fields of human endeavor. …”
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Artificial Neural Controller Synthesis in Autonomous Mobile Cognition by Kim On Chin, Jason Teo

“…The Pareto-frontier Differential Evolution (PDE) algorithm is utilized to generate the Pareto optimal sets through a 3-layer feed-forward artificial neural network that optimize the conflicting objectives of robot behavior and network complexity, where the two different types of robot behaviors are phototaxis and RF-localization, respectively. …”
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Artificial Neural Controller Synthesis in Autonomous Mobile Cognition by Chin Kim On, Jason Teo

“…The Pareto-frontier Differential Evolution (PDE) algorithm is utilized to generate the Pareto optimal sets through a 3-layer feed-forward artificial neural network that optimize the conflicting objectives of robot behavior and network complexity, where the two different types of robot behaviors are phototaxis and RF-localization, respectively. …”
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Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm by Noor Azam, Muhammad Harith, Ridzuan, Farida, Mohd Sayuti, M Norazizi Sham

“…One of the search methods commonly used to find solutions for the trade-off problem in various fields is the Multi-Objective Evolutionary Algorithm (MOEA). …”
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Evolutionary Fuzzy ARTMAP Neural Networks for Classification of Semiconductor Defects by Zuwairie, Ibrahim, Tan, Shing Chiang, Watada, Junzo, Marzuki, Khalid

“…Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. …”
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A hybrid optimization technique for solving economic emission load dispatch problems by Aisyah binti Nordin

Optimization of energy management and conversion in the water systems based on evolutionary algorithms by Karami, Hojat, Ehteram, Mohammad, Mousavi, Sayed-Farhad, Farzin, Saeed, Kisi, Ozgur, El-Shafie, Ahmed

“…In addition, genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have been used as successful algorithms in such engineering application for optimizations in order to introduce a comprehensive comparison analysis with the results of WOA. …”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio... by Daba, Layth Muwafaq Abdulhussein

“…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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Investigation of evolutionary multi-objective algorithms in solving view selection problem / Seyed Hamid Talebian by Talebian, Seyed Hamid

“…Generally, it can be said that two algorithms SPEA-II and NSGA-II perform better than other algorithms in terms of convergence to the optimal solution and diversity.…”
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Hybrid flow shop scheduling problem with energy utilization using non-dominated sorting genetic algorithm-III (NSGA-III) optimization by Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

“…The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). …”
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A hybrid bat–swarm algorithm for optimizing dam and reservoir operation by Yaseen, Zaher Mundher, Allawi, Mohammed Falah, Karami, Hojat, Ehteram, Mohammad, Farzin, Saeed, Ahmed, Ali Najah, Koting, Suhana, Mohd, Nuruol Syuhadaa, Jaafar, Wan Zurina Wan, Afan, Haitham Abdulmohsin, El-Shafie, Ahmed

“…The proposed HB-SA is validated by minimizing irrigation deficits using a multireservoir system consisting of the Golestan and Voshmgir dams in Iran. In addition, different optimization algorithms from previous studies are investigated to compare the performance of the proposed algorithm with existing algorithms for the same case study. …”
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A hybrid bat�swarm algorithm for optimizing dam and reservoir operation by Yaseen Z.M., Allawi M.F., Karami H., Ehteram M., Farzin S., Ahmed A.N., Koting S.B., Mohd N.S., Jaafar W.Z.B., Afan H.A., El-Shafie A.

Varying Variants For AncDE With MDV Between Target And Trial Vector Measurement by Abal Abas, Zuraida, Mohd Salleh, Siti Khadijah, O’Donoghue, Diarmuid, Shibghatullah, Abd Samad

“…This paper compares standard Differential Evolution algorithm with AncDE,which adds a separate cache of recent ancestors that serve as an additional source of highquality genetic information.We compare the solutions produced by both DE and AncDE algorithms using benchmarks of 15 different numeric optimisation problems.Two distinct explorations are presented.The first test is distinct algorithmic variants of AncDE.The second part of this paper defines an MDV attribute and results are presented indicating some interesting differences in MDV between the DE and AncDE algorithms.Our findings indicate that ancestors can help to overcome some of the local variations in solutions quality and improve solution quality by improving population diversity.…”
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Impact of genetic operators on energy-efficient wireless sensor network by Vincent Chung, Norah Tuah, Lim, Kit Guan, Tan, Min Keng, Huang, Hui, Teo, Kenneth Tze Kin

“…The metaheuristic genetic algorithm is an evolutionary algorithm which means that it will always evolve to get an optimum solution. …”
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Dual optimization approach in discrete Hopfield neural network by Guo, Yueling, Zamri, Nur Ezlin, Mohd Kasihmuddin, Mohd Shareduwan, Alway, Alyaa, Mansor, Mohd. Asyraf, Li, Jia, Zhang, Qianhong

“…To evaluate the effectiveness of the Hybrid Differential Evolution Algorithm and Swarm Mutation in the learning and retrieval phases, several performance metrics are employed in terms of synaptic weight management, learning errors, testing errors, energy profiles, solution variations, and similarity for 10 different cases. …”
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Power system intentional islanding for different contingency scenarios using discrete optimization technique by Nur Zawani Binti Saharuddin

“…The initial intentional islanding solution was determined using graph theory approach, to facilitate the proposed MDEP algorithm in determining the optimal intentional islanding strategy. …”

Channel-aware downlink scheduling for quality of service in long term evolution network by Najim, Alaa Omer

“…These constraints comprise the use of an inefficient method to determine delay by the buffer size. In the proposed algorithm we, utilize Head of Line delay with channel-related parameters such as user data rate in addition to several pre-defined QoS tunable parameters to formulate a rule that prioritizes different user flows assigned with channel resources in order to improve delay. …”
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Enhancing solid oxide fuel cell efficiency through advanced model identification using differential evolutionary mutation fennec fox algorithm by Singla, Manish Kumar, Gupta, Jyoti, Kumar, Ramesh, Jangir, Pradeep, Louzazni, Mohamed, Giri, Nimay Chandra, Al Gburi, Ahmed Jamal Abdullah, EI‑Kenawy, E. I.‑Sayed M., Alharbi, Amal H.

“…This research introduces a novel approach for optimal SOFC model identification using a differential evolutionary mutation Fennec fox algorithm (DEMFFA). …”
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An improved component carrier selection algorithm for downlink long term evolution advanced by Mohd. Ramli, Huda Adibah, Mohd. Isa, Farah Nadia, Asnawi, Ani Liza

“…CA is a method that aggregates multiple Component Carriers (CCs) of the same or different frequency bands. Additionally, LTE-A system needs to simultaneously support a mixture of LTE-A and Long Term Evolution (LTE) users. …”
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