Environmental/economic dispatch using fuzzy logic controlled genetic algorithms

1997 ◽  
Vol 144 (4) ◽  
pp. 377 ◽  
Author(s):  
Y.H. Song ◽  
G.S. Wang ◽  
P.Y. Wang ◽  
A.T. Johns
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Environmental Economic ◽  
Economic Dispatch

scholarly journals A Novel Parallel SPEA2 for Solving the Environmental-Economic Dispatch Problem: Competitive vs Cooperative Approach

2020 ◽  
Author(s):  
João Pedro Augusto Costa ◽  
Omar Andres Carmona Cortes ◽  
Osvaldo Ronald Saavedra
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Environmental Economic ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Pareto Frontier ◽  
Parallel Model ◽  
Swarm Optimization ◽  
Cooperative Approach ◽  
Economic Dispatch Problem

This paper aims to compare two different parallel approaches (cooperative and competitive) of the SPEA2 for solving the environmental-economic dispatch problem. The idea is to solve the problem by executing the SPEA2 algorithm along with three different meta-heuristics (Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution) to perform changes in the population. The different meta-heuristics work in parallel using two different approaches. The first one is the competitive approach, in which meta-heuristics compete for producing the best set of candidate solutions for solving the problem. Whereas, the cooperative approach selects the new population merging all individuals from all meta-heuristics, then selecting the solution set for the Pareto frontier. The proposal was implemented in C++ using MPI in a master-slave parallel model. Two  study cases were used: the first one with six generators and the second one with forty generators. Results showed that the cooperative approach presented the best Pareto frontier for the case of 40 generators.

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.

scholarly journals Summaries of certain spatial patterns retrieved from multidate remote-sensing data

2004 ◽  
Vol 2004 (2) ◽  
pp. 287-300
Author(s):  
Hema Nair
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Spatial Patterns ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Geographic Area ◽  
Image Mining ◽  
Sensing Data

This paper presents an approach to describe patterns in remote-sensed images utilising fuzzy logic. The truth of a linguistic proposition such as “Y isF” can be determined for each pattern characterised by a tuple in the database, where Y is the pattern andFis a summary that applies to that pattern. This proposition is formulated in terms of primary quantitative measures, such as area, length, perimeter, and so forth, of the pattern. Fuzzy descriptions of linguistic summaries help to evaluate the degree to which a summary describes a pattern or object in the database. Techniques, such as clustering and genetic algorithms, are used to mine images. Image mining is a relatively new area of research. It is used to extract patterns from multidated satellite images of a geographic area.

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