Search Results - a distribution ((function algorithm) OR (_ algorithm))

Embedded parallel distributed artificial intelligent processors for adaptive beam forming in WCDMA system by Krishnan P.S., Kiong T.S., Koh J., Yap D.

“…In this paper, an embedded parallel and distributed genetic algorithm (EPDGA) with dynamic parameter setting on a multiprocessor system is proposed. …”

Levy tunicate swarm algorithm for solving numerical and real-world optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

“…The proposed Levy Tunicate Swarm Algorithm (LTSA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic algorithm called Tunicate Swarm Algorithm (TSA) to solve numerical and real-world optimization problems. …”
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Levy slime mould algorithm for solving numerical and engineering optimization problems by J. J., Jui, M. A., Ahmad, M. I. M., Rashid

“…The proposed Levy Slime Mould Algorithm (LSMA) is a novel metaheuristic algorithm that integrates the Levy distribution into a new metaheuristic called Slime Mould Algorithm (SMA) for solving numerical and engineering problems. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…Thereafter, a multi-objective hybrid algorithm (MOHA), an extension of the self-adaptive hybrid algorithm is proposed and tested on the established multi-objective (MO) test functions. …”
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An empirical study of density and distribution functions for ant swarm optimized rough reducts by Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah

“…However, the description and comparison of both functions are very limited. Hence, this paper compares the solution vector of ACO is represented by both density and distribution function to search for a better solution and to specify a probability functions for every particle (ant), and generate components of solution vector, which satisfy Gaussian distributions. …”
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A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms by Pesaran H.A M., Huy P.D., Ramachandaramurthy V.K.

Distributed generation allocation to reduce losses and improve voltage profile genetic algorithm: article by Abdol Azis, Hazriq Syahmie

“…This paper presents a method for the optimal allocation of Distributed generation in distribution systems. …”
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Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network by Bujal, Noor Ropidah

“…There is a need to achieve optimality in allocating and sizing of DG in the distribution system network. …”
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Slice sampler algorithm for generalized pareto distribution by Rostami, Mohammad, Adam, Mohd Bakri, Yahya, Mohamed Hisham, Ibrahim, Noor Akma

“…In this paper, we developed the slice sampler algorithm for the generalized Pareto distribution (GPD) model. …”
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A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

“…The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. …”
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Offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm: article / Deezex Noor Ainizaa Abdullah by Abdullah, Deezex Noor Ainizaa

“…The study involves the development of Artificial Bee Colony (ABC) algorithm to implement loss minimization in a distribution system. …”
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Offline study on loss optimization in distribution networks using Artificial Bee Colony (ABC) algorithm / Deezex Noor Ainizaa Abdullah by Abdullah, Deezex Noor Ainizaa

“…The study involves the development of Artificial Bee Colony (ABC) algorithm to implement loss minimization in a distribution system. …”
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Multi-objective spiral dynamic algorithms-based for a better accuracy and diversity by Ahmad Azwan, Abdul Razak

“…All the developed algorithms were tested with a set of benchmark functions comprising of 10 different functions covering various fitness landscapes and features. …”
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Estimation of the denitrification in the lower Mississippi Valley by Saleh, Abdul Razak

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Network reconfiguration and control for loss reduction using genetic algorithm by Jawad, Mohamed Hassan Izzaldeen

“…In this work a feeder reconfiguration algorithm is presented for the purpose of power loss reduction in distribution networks. …”
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Optimal network reconfiguration and intelligent service restoration prediction technique based on Cuckoo search spring algorithm / Mohamad Izwan Zainal by Zainal, Mohamad Izwan

“…In addition, objective function using the same CSSA algorithm were applied i.e., Vmin and Ploss as the objective function, and multi-objective involves Vmin and Ploss as the objective function. …”
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Novel distributed algorithm for coalition formation for enhanced spectrum sensing in cognitive radio networks by Tahir, Mohammad, Habaebi, Mohamed Hadi, Islam, Md Rafiqul

“…In this paper, we propose a novel algorithm for improving spectrum sensing in cognitive radio networks by forming coalitions among cognitive radio users in a fading channel environment. …”
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Improved gravitational search algorithm for optimal placement and sizing of distributed generation for power quality enhancement by Daud, Sa'adah

“…The optimization problem is generated using a weighted sum method. In order to obtain the best compromise solution, a novel heuristic algorithm based on improved gravitational search algorithm (IGSA) is proposed as an optimization technique. …”
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Optimal Location And Sizing Of Distrubuted Generator Using PSO And GA Algorithms In Power Systems by Hassan, Ayat Saleh

“…This research aimed to reduce total power losses and improve voltage profiles of the distribution system by proposing a practical swarm optimizion algorithm GA genetic algorithm to optimize DG size and location by taking into consideration increase number of DG units in the system. …”
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Hierarchical Bayesian estimation for stationary autoregressive models using reversible jump MCMC algorithm by Suparman, S., Rusiman, Mohd Saifullah

“…In the hierarchical Bayesian approach, the order and coefficients of the autoregressive model are assumed to have a prior distribution. The prior distribution is combined with the likelihood function to obtain a posterior distribution. …”
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