Particle Swarm Optimization and Varying Chemotactic Step-Size Bacterial Foraging Optimization Algorithms Based Dynamic Economic Dispatch with Non-smooth Fuel Cost Functions

2010 ◽  
pp. 727-738 ◽  
Author(s):  
P. Praveena ◽  
K. Vaisakh ◽  
S. Rama Mohana Rao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Cost Functions ◽  
Fuel Cost ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Dynamic Economic Dispatch

scholarly journals Dynamic economic dispatch: a comparative study for differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Comparative Study ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Evolutionary Programming ◽  
Swarm Optimization ◽  
Dynamic Economic Dispatch

AbstractThis paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

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