Search Results - pareto evaluation method algorithm
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Evaluating the effectiveness of integrated benders decomposition algorithm and epsilon constraint method for multi-objective facility location problem under demand uncertainty
Published 2017“…One of the most challenging issues in multi-objective problems is finding Pareto optimal points. This paper describes an algorithm based on Benders Decomposition Algorithm (BDA) which tries to find Pareto solutions. …”
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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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Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling
Published 2022“…The IEEE RTS 26, 32 and 36-unit dataset systems were used in the performance evaluation of the PACS algorithm. The performance of PACS algorithm was compared against four benchmark multi-objective algorithms including the Nondominated Sorting Genetic, Strength Pareto Evolutionary, Simulated Annealing, and Particle Swarm Optimization using the metrics grey relational grade (GRG), coverage, distance to Pareto front, Pareto spread, and number of non-dominated solutions. …”
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A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks / Salmah Fattah
Published 2022“…The algorithm introduces the fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method when one solution cannot dominate the other in terms of the fuzzy dominance level. …”
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A hybrid multi-objective optimisation for energy efficiency and better coverage in underwater wireless sensor networks
Published 2022“…The algorithm introduces the fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method when one solution cannot dominate the other in terms of the fuzzy dominance level. …”
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Application of Multi-objective Genetic Algorithm (MOGA) optimization in machining processes
Published 2020“…Multi-objectives Genetic Algorithm (MOGA) is one of many engineering optimization techniques, a guided random search method. …”
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Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
Published 2020“…The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. …”
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Hybrid multi-objective optimization methods for in silico biochemical system production
Published 2016“…The proposed method combined Newton method, Strength Pareto approach, Cooperative Coevolutionary Algorithm (CooCA) and Genetic Algorithm (GA). …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…It was superior to the clustering algorithm methods in most real-world datasets with means ARI of over 0.35. …”
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Standardizing and weighting the evaluation criteria of many-objective optimization competition algorithms based on fuzzy delphi and fuzzy-weighted zero-inconsistency methods
Published 2021“…The evaluation criteria of Many Objective Optimization algorithm (MaOO) play a critical role in evaluating the competition MaOO algorithms. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems
Published 2020“…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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Backpropagation neural network based on local search strategy and enhanced multi-objective evolutionary algorithm for breast cancer diagnosis
Published 2019“…The network size and accuracy results of the proposed method are better than the previous methods. Therefore, the method is then capable of finding a proper number of hidden neurons and error rates of the BP algorithm.…”
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A MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR WELLBORE TRAJECTORY DESIGN
Published 2021“…Better performances were observed in cases of Pareto optimal front, search capabilities, and diversity of solutions by comparing the proposed method with other standard methods like MOCPSO, MOGWO, and MOPSO. …”
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Multi-objective harmony search algorithm for capacitated location-routing problem of reverse logistics using fuzzy membership approach
Published 2024“…The best method is determined by the evaluation of performance metric. …”
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A Hybrid of Optimization Method for Multi-Objective Constraint Optimization of Biochemical System Production
Published 2015“…The proposed method combines Newton method, Strength Pareto Evolutionary Algorithm (SPEA) and Cooperative Co-evolutionary Algorithm (CCA). …”
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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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Reservoir system modelling using nondominated sorting genetic algorithm in the framework of climate change
Published 2014“…In conclusion, this finding contributes toward the development of models using evolution algorithm and statistical methods for sustainable water resources planning and management in the context of future climate change…”
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Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy
Published 2024“…Advancing multi-objective optimization techniques for cancer treatment strategies, the study strategically incorporates Swarm Intelligence (SI) and Evolutionary Algorithms (EA). Diverging in constraints handling and Pontryagin Maximum Principle (PMP) application, the PMM and HM methodologies are evaluated through the lens of SI and EA, and their outcomes are visualized via the Pareto Optimal Front (PF). …”
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