Search Results - pareto distribution ((impact algorithm) OR (path algorithm))

NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment by Shen, jiazheng, Tang, Sai Hong, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan, Wang, Xinming

“…By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. …”
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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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Extreme air pollutant data analysis using classical and Bayesian approaches by Mohd Amin, Nor Azrita

“…Generalized extreme value (GEV) distribution and generalized Pareto (GP) distribution are two main models in EV theory based on block maxima and threshold exceedances approaches. …”
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Multi-objective scientific workflow scheduling algorithm in multi-cloud environment for satisfying QoS requirements by Ramadhan, Mazen Farid Ebrahim

“…Third, to propose a fault-intrusion-tolerant algorithm (FITSW), which is based on both fault and intrusion-tolerant techniques, to decrease the adverse impact caused by different faults (accidental and malicious) in cloud computing systems. …”
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Multi-objective portfolio selection with skewness preference: An application to the stock and electricity markets / Karoon Suksonghong by Karoon, Suksonghong

“…The superiority of this method is its ability to generate a set of MVS efficient portfolios within a single run of algorithm. The non-dominated sorting genetic algorithm II (NSGA-II), the improved strength Pareto evolutionary algorithm II (SPEA-II), and the compressed objective genetic algorithm II (COGA-II) were applied. …”
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4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer by Ahmed, R., Mahadzir, S., Mota-Babiloni, A., Al-Amin, M., Usmani, A.Y., Ashraf Rana, Z., Yassin, H., Shaik, S., Hussain, F.

“…Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. …”
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