Search Results - (( pareto distribution methods algorithm ) OR ( parameter evaluation method algorithm ))
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A memory-guided Jaya algorithm to solve multi-objective optimal power flow integrating renewable energy sources
Published 2025Subjects:Review -
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Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
Published 2016“…This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. …”
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Advanced Pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
Published 2016“…This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. …”
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Location and sizing of distributed generation photovoltaic (DGPV) via multi-objective pareto algorithm
Published 2023Conference Paper -
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Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system
Published 2020“…Loss of load probability (LLP) and life-cycle cost (LLC) are considered to obtain the Pareto front. The iterative method is employed for validation of the superiority results of the proposed MS-MOSS algorithm. …”
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Optimal placement and sizing of renewable distributed generations and capacitor banks into radial distribution systems
Published 2017“…First a set of non-dominated Pareto-front data are called from the algorithm. Later, a fuzzy decision technique is applied to extract the trade-off solution set. …”
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Hybrid flow shop scheduling problem with energy utilization using non-dominated sorting genetic algorithm-III (NSGA-III) optimization
Published 2023“…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
Published 2015“…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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Review on bio-inspired algorithms approach to solve assembly line balancing problem
Published 2019“…Assembly line balancing problem is very crucial to solve since it involves minimizing the time of the machines and operators that required optimal task distribution. The outcome of this paper shows the effectiveness of bio-inspired algorithms in solving assembly line balancing problem compared to traditional method.…”
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Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…In addition, two novel Jaya-based methods namely, the modified Jaya (MJaya) algorithm and quasi-oppositional modified Jaya (QOMJaya) algorithm are proposed to solve different MOOPF problems. …”
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Bi-objective mixed integer nonlinear programming model for low carbon location-inventory-routing problem with time windows and customer satisfaction
Published 2024“…The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. …”
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Mathematical models and optimization algorithms for low-carbon Location-Inventory-Routing Problem with uncertainty
Published 2024“…Further more, an improved non-dominated sorting genetic algorithm with an elite strategy II (IMNSGA-II) has been developed to solve the two bi-objective models, surpassing existing literature’s algorithms such as Pareto Envelope-based Selection Algorithm II (PESA-II) and NSGA-II. …”
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Parameter extraction of photovoltaic module using hybrid evolutionary algorithm
Published 2023Conference Paper -
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A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization
Published 2022“…Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. …”
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Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm
Published 2023“…The gray relational grade (GRG) performance metric and the Friedman test were used to evaluate the algorithm’s performance. The Taguchi-GRA method that produced the new values for the algorithm’s parameters was shown to be able to provide a better multi-objective generator maintenance scheduling (GMS) solution. …”
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Evaluation of lightning current parameters using measured lightning induced voltage on distribution power lines
Published 2019“…In this paper, an algorithm had been proposed to evaluate the lightning current parameters using measured voltage from overhead distribution lines based on lightning location obtained from lightning location system. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…This comparative study aims to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…This comparative study aims to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. …”
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