Search Results - (( pressure distribution swarm algorithm ) OR ( parameters variation bees algorithm ))

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  1. 1
    Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control

    Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control by Naidu, K., Mokhlis, Hazlie, Bakar, Ab Halim Abu

    Published 2014
    “…Furthermore, the proposed algorithm is robust enough to operate under different operating conditions and system parameter variations.…”
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  2. 2
    Neuro-Fuzzy and Particle Swarm Optimization based Model for the Steam and Cooling sections of a Cogeneration and Cooling Plant

    Neuro-Fuzzy and Particle Swarm Optimization based Model for the Steam and Cooling sections of a Cogeneration and Cooling Plant by Alemu Lemma, Tamiru, Rangkuti, Chalillullah, Mohd Hashim, Fakhruldin

    Published 2009
    “…Neuro-fuzzy approach trained by a sequence of optimization algorithms-Particle Swarm Optimization (PSO) followed by Back-Propagation (BP)-is used to develop models for the steam drum pressure, steam drum water level, steam flow rate and chilled water supply temperature. …”
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  3. 3
    Optimal variational mode decomposition and integrated extreme learning machine for network traffic prediction

    Optimal variational mode decomposition and integrated extreme learning machine for network traffic prediction by Jinmei Shi, Yu-Beng Leau, Kun Li, Huandong Chen

    Published 2021
    “…A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. …”
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  4. 4
    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar

    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

    Published 2019
    “…There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient (�436�45D ). …”
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  5. 5
    Optimization of pressure vessel design using pyopt

    Optimization of pressure vessel design using pyopt by Woldemichael, D.E., Woldeyohannes, A.D.

    Published 2016
    “…In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. …”
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  6. 6
    Optimization of pressure vessel design using pyopt

    Optimization of pressure vessel design using pyopt by Woldemichael, D.E., Woldeyohannes, A.D.

    Published 2016
    “…In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. …”
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  7. 7
    Determining penetration limit of central distributed generation topology in radial distribution networks

    Determining penetration limit of central distributed generation topology in radial distribution networks by Suliman, Mohamed Saad Abdelgadir

    Published 2021
    “…The biogeography based optimization method has been proven to have better performance than artificial bee colony, genetic algorithm, particle swarm optimization, hybrid of particle swarm optimization and constriction factor approach, and hybrid of ant colony optimization and artificial bee colony methods in terms of active power loss reduction. …”
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