نتایج جستجو برای: dominate sorting genetic algorithm ii

تعداد نتایج: 1873964  

Journal: :IEEE Access 2021

Combining classical technologies with modern intelligent algorithms, this paper introduces a new approach for the optimisation and modelling of EAF-based steel-making process based on multi-objective using evolutionary computing machine learning. Using large amount real-world historical data containing 6423 consecutive EAF heats collected from melt shop in an established steel plant work not on...

Journal: :Mathematics 2023

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According characteristics and considering power level battery capacity electric vehicles, multi-objective immune genetic algorithm (MOIGA) designed compared with an elitist strategy algorithm...

Journal: :Mechanism and Machine Theory 2022

• Macro and micro-geometry parameters are used as decision variables. Study of the impact taking complete transmission by two approaches. The using a genetic algorithm NSGA-II. This study aims to implement multi-objective optimization gear unit in order minimize power loss vibrational excitation generated meshing, via multi-scale approach that extends from contact transmission. All these indica...

2017
Amit Prakash Karamjit Bhatia Raj Kumar

Task scheduling is a crucial issue in distributed (disbursed) heterogeneous processing environment and significantly influence the performance of the system. The task scheduling problem has been identified to be NP-complete in its universal frame. In this paper the task scheduling problem is investigated using multiple-objective particle (molecule) swarm optimization algorithm with crowded disp...

2006
Hamidreza Eskandari Christopher D. Geiger

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FPGA). FPGA uses a new ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally expensive. New genetic operators are employed to enhance the algorithm’s performance in terms of convergence behavior and computational effort. Compu...

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