Search Results - (( using (evolutionary OR evolution) bat algorithm ) OR ( wave optimization method algorithm ))

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    Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty by Ehteram, Mohammad, Mousavi, Sayed Farhad, Karami, Hojat, Farzin, Saeed, Singh, Vijay P., Chau, Kwok Wing, El-Shafie, Ahmed

    Published 2018
    “…Results showed the volume of water to be released for the future period, based on all evolutionary algorithms used, was less than for the base period, and the bat algorithm with high-reliability index and low vulnerability index performed better among other evolutionary algorithms.…”
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    Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions by Abbas, Iraq Tareq

    Published 2018
    “…The use of the BOGSA algorithm aims to create a new equation for the calculation of the masses of population individuals, as found in the theoretical work in the Strength Pareto Evolutionary Algorithm two (SPEAII) algorithm. …”
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    Enhancing benchmark optimization with evolutionary random approach: A comparative analysis of modified adaptive bats sonar algorithm (MABSA) by Nor Shuhada, Ibrahim, Nafrizuan, Mat Yahya, Saiful Bahri, Mohamed, Mohd Ismail, Yusof

    Published 2025
    “…This article presents a novel hybrid algorithm, combining the Modified Adaptive Bats Sonar Algorithm (MABSA) with the Squirrel Search Algorithm (SSA), and compares its performance with the original MABSA. …”
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    Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation by Hamimu, La

    Published 2011
    “…Therefore the use of genetic algorithm (GA) optimization technique which is one of nonlinear optimization methods is an appropriate choice to solve surface wave inversion problem having high nonlinearity and multimodality. …”
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    Optimizing the efficiency of Oscillating Water Column (OWC) wave energy converter using genetic algorithm by Nallagownden, P., Alhaj, H.M.M., Sarwar, M.B.

    Published 2015
    “…This paper, describes a method to maximize the pneumatic system efficiency using optimization technique based on Genetic algorithm. …”
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    Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G by Salh, Adeb, Audah, Lukman, Abdullah, Qazwan, Aydoğdu, Ömer, A. Alhartomi, Mohammed, Alsamhi, Saeed Hamood, A. Almalki, Faris, M. Shah, Nor Shahida

    Published 2021
    “…We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). …”
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  9. 9

    Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G by Salh, Adeb, Audah, Lukman, Abdullah, Qazwan, Aydoğdu, Ömer, A. Alhartomi, Mohammed, Alsamhi, Saeed Hamood, A. Almalki, Faris, M. Shah, Nor Shahida

    Published 2021
    “…We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). …”
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    Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

    Published 2018
    “…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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    Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

    Published 2018
    “…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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  13. 13

    Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management by Julius, Beneoluchi Odili, M. N. M., Kahar, Noraziah, Ahmad, M., Zarina, Riaz, Ul Haq

    Published 2017
    “…After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. …”
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    Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

    Published 2017
    “…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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    A generalized laser simulator algorithm for optimal path planning in constraints environment by Aisha, Muhammad

    Published 2022
    “…The results demonstrated that the proposed method could generate an optimal collision-free path. …”
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    Interferometric array planning using division algorithm for radio astronomy applications by Kiehbadroudinezhad, Shahideh

    Published 2017
    “…In the second scheme, a genetic algorithm is developed, in order to optimize a correlator array of antennas by using Genetic Algorithm (GA). …”
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    Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application by Ahmed, Marzia, Mohd Herwan, Sulaiman, Ahmad Johari, Mohamad, Rahman, Mostafijur

    Published 2024
    “…In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. …”
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