Ant Colony Search, Advanced Engineered-Conditioning Genetic Algorithms and Fuzzy Logic Controlled Genetic Algorithms: Economic Dispatch Problems

1999 ◽  
pp. 247-260 ◽  
Author(s):  
Y. H. Song ◽  
C. S. V. Chou ◽  
I. K. Yu ◽  
G. S. Wang
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Economic Dispatch ◽  
Ant Colony

Solving Fuzzy Combined Economic Load–Emission Dispatch by Using Differential Evolution Immunized Ant Colony Optimization Technique

2015 ◽  
Vol 785 ◽  
pp. 500-505
Author(s):  
Nur Azzammudin Rahmat ◽  
Ismail Musirin ◽  
Ahmad Farid Abidin
Keyword(s):  
Fuzzy Logic ◽  
Differential Evolution ◽  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Ant Colony ◽  
Time Varying ◽  
Load Demand ◽  
Dynamic Economic Dispatch ◽  
Emission Dispatch

The fluctuating load demand with respect to time requires thorough analysis by the energy providers. Nowadays, the utilities need to include emission control during energy dispatch planning. The practically accurate economic dispatch solution is achieved by considering it as a dynamic or time-varying problem. Therefore, this research proposes Fuzzy Combined Economic Load-Emission Dispatch (Fz-CELED) to solve dynamic economic dispatch problem. Fuzzy Logic is used to predict the future load demand and fuel pricing. Moreover, this paper proposed the Differential Evolution Immunized Ant Colony Optimization (DEIANT) technique to optimize Fz-CELED problem. The research was conducted on IEEE 57-Bus Systems. Comparative studies are also conducted among DEIANT, ACO and EP to assess their performance.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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