So far, numerous scientific works h

So far, numerous scientific works have been developed by
researchers dealt with the optimal operation scheduling under
different loading conditions and objectives. At first, conventional
economic scheduling has been proposed as a solution for the
optimization problem through finding an optimal set of generators
to satisfy load demand and operational constraints in an economical manner [6e8]. Due to the environmental concerns and
pollutants emission from traditional fossil fuel units, singleobjective optimization could no longer be satisfactory in the
mentioned problem. To involve emission as a separate goal, multiobjective optimization techniques have been developed in articles
in order to choose a definite number of units for supplying the load
under a certain condition taking into account minimum levels of
cost and emission for grid operation [9e13]. Recently, evolutionary
algorithms such as GA (Genetic Algorithm), PSO (Particle Swarm
Optimization) and so on have been increasingly proposed for
solving the optimization problem because of their inherent
nonlinear mapping, simplicity and powerful search capabilities
[13e18]. Hybrid approaches such as Fuzzy-based evolutionary

ผลลัพธ์ (อังกฤษ) 2:

So Far, Scientific numerous works have been developed by
researchers dealt with the Optimal Operation scheduling under
different loading conditions and objectives. At First, conventional
Economic scheduling has been as a Proposed Solution for the
Problem Through Optimization Finding an Optimal SET of generators
to Satisfy Load demand and operational constraints in an economical manner [6e8]. Due to the Environmental concerns and
pollutants emission from Traditional Fossil fuel Units, Singleobjective Optimization could no Longer be Satisfactory in the
mentioned Problem. To involve emission as a Separate Goal, Multiobjective Optimization techniques have been developed in articles
in Order to choose a definite Number of Units for supplying the Load
Into Account under a certain condition taking minimum levels of
cost and emission for Grid Operation [9e13]. Recently, Evolutionary
algorithms such as GA (Genetic Algorithm), PSO (Particle Swarm
Optimization) and so on have been increasingly Proposed for
solving the Problem Optimization because of their inherent
nonlinear Mapping, simplicity and powerful search capabilities
[13e18]. Hybrid approaches such as Fuzzy-based evolutionary.

ผลลัพธ์ (อังกฤษ) 3:

So far numerous scientific, works have been developed by.Researchers dealt with the optimal operation scheduling under.Different loading conditions and objectives. At first conventional,,Economic scheduling has been proposed as a solution for the.Optimization problem through finding an optimal set of generators.To satisfy load demand and operational constraints in an economical manner [6e8]. Due to the environmental concerns and.Pollutants emission from traditional fossil fuel units singleobjective optimization, could no longer be satisfactory in. The.Mentioned problem. To involve emission as a separate goal multiobjective optimization, techniques have been developed in. Articles.In order to choose a definite number of units for supplying the load.Under a certain condition taking into account minimum levels of.Cost and emission for grid operation []. Recently evolutionary 9e13,,Algorithms such as GA (Genetic Algorithm), PSO (Particle Swarm.Optimization) and so on have been increasingly proposed for.Solving the optimization problem because of their inherent.Nonlinear mapping simplicity and, powerful search capabilities.[]. Hybrid 13e18 approaches such as Fuzzy-based evolutionary.