Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques
(Sprache: Englisch)
Four modified versions of particle swarm optimizer (PSO) have been applied to the economic power dispatch with valve-point effects. In order to obtain the optimal solution, traditional PSO search a new position around the current position. The proposed...
Voraussichtlich lieferbar in 3 Tag(en)
versandkostenfrei
Buch (Kartoniert)
34.99 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenlose Rücksendung
Produktdetails
Produktinformationen zu „Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques “
Klappentext zu „Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques “
Four modified versions of particle swarm optimizer (PSO) have been applied to the economic power dispatch with valve-point effects. In order to obtain the optimal solution, traditional PSO search a new position around the current position. The proposed strategies which explore the vicinity of particle s best position found so as far leads to a better result. In addition, to deal with the equality constraint of the economic dispatch problems, a simple mechanism is also devised that the difference of the demanded load and total generating power is evenly shared among units except the one reaching its generating limit. To show their capability, the proposed algorithms are applied to thirteen.Comparision among particle swarm optimization is given. The results show that the proposed algorithms indeed produce more optimal solutions in both cases.The different PSO techniques are New PSO, Self-Adaptive PSO and Chaotic PSO Among the different PSO techniques, it is found that Self-Adaptive PSO is better than other PSO techniques in terms of better solutions, speed of convergence, time of execution and robustness but it has more premature convergence.
Lese-Probe zu „Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques “
Chapter 3.1, Evolutionary Algorithm:An evolutionary algorithm (EA) is the subset of evolutionary computation, a generic population - based metaheuristic optimization algorithm. An EA uses some mechanism inspired by biological evolution: reproduction, mutation, recombination, natural selection and survival of the fittest. Candidate solutions to the optimization problem play the role of individuals in a population, and the cost function determines the environment within which the solution live (see also fitness function) . Evolution of the population then takes place after the repeated application of the above operators. Artificial evolution (AE) describes a process involving individual evolutionary algorithm; EAs are individual components that participate in artificial evolutions.
EAs consistently perform well approximating solutions to all types of problems because they don t make any assumption about the underlying fitness landscape; this generality is show by successes in fields as diverse as engineering, art, biology, economics, genetic, operations research, robotics, social sciences, physics and chemistry. However, evolutionary algorithms can nonetheless the outperformed by more field - specific algorithm.
Apart from their use as mathematical optimizers, evolutionary computation and algorithms and been used as an experimental frame work within which to validate theories about biological evolution and natural selection, particularly through work in field of artificial life. Techniques from evolutionary algorithm applied to the modeling of biological evolution are generally limited to explorations of micro evolutionary processes. A limitation of evolutionary algorithms is their lack of clear genotype - phenotype distinction. In nature, the fertilized egg cell undergoes a complex process know as embryogenesis to become a mature phenotype. This indirect encoding is believed to make genetic search more robust (i.e. reduce the probability of fatal mutations), and
... mehr
also may improve the resolvability of the organism. Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns.
Four evolutionary methods are used in this project they are as follows PSO, APSO, CPSO and NPSO, these algorithms are discussed in detail.
Chapter 3.2, Ant Colony Optimization:
In the real world, ants (initially) wander randomly, and upon finding food return to their colony while laying down phenomenon trails. If other ants find such a path, they are not likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find the source of food.
Over time, however the phenomenon trails starts to evaporate thus reducing the attractive strength. The more time it takes from an ant to travel down the path and back again, the more time the phenomenons have to evaporate. A short path by comparison, gets marched over faster, and thus the phenomenon density remains high as it is laid on the path as fast as it can evaporate. Phenomenon evaporation has also the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the path chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained.
Thus, one ant finds a good (short, in other words) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback algorithm is to mimic this behavior with simulated ants walking around the graph representing the problem to solve.
Ant colony optimization algorithms have been used to produce near - optimal solutions to the travelling salesman problem. They have an advantage over simulated annealing and genetic algorithm approaches when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in
Four evolutionary methods are used in this project they are as follows PSO, APSO, CPSO and NPSO, these algorithms are discussed in detail.
Chapter 3.2, Ant Colony Optimization:
In the real world, ants (initially) wander randomly, and upon finding food return to their colony while laying down phenomenon trails. If other ants find such a path, they are not likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find the source of food.
Over time, however the phenomenon trails starts to evaporate thus reducing the attractive strength. The more time it takes from an ant to travel down the path and back again, the more time the phenomenons have to evaporate. A short path by comparison, gets marched over faster, and thus the phenomenon density remains high as it is laid on the path as fast as it can evaporate. Phenomenon evaporation has also the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the path chosen by the first ants would tend to be excessively attractive to the following ones. In that case, the exploration of the solution space would be constrained.
Thus, one ant finds a good (short, in other words) path from the colony to a food source, other ants are more likely to follow that path, and positive feedback algorithm is to mimic this behavior with simulated ants walking around the graph representing the problem to solve.
Ant colony optimization algorithms have been used to produce near - optimal solutions to the travelling salesman problem. They have an advantage over simulated annealing and genetic algorithm approaches when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time. This is of interest in
... weniger
Autoren-Porträt von Vikramarajan Jambulingam
Mr. J. Vikramarajan holds a Master degree in Power Electronics and Drives and Bachelor degree in Electrical and Electronics Engineering from VIT University in India. His research interests are power electronic applications, power quality, power electronic converters and power electronic controllers for renewable energy systems.
Bibliographische Angaben
- Autor: Vikramarajan Jambulingam
- 2014, Erstauflage, 62 Seiten, 11 Abbildungen, Maße: 15,5 x 22 cm, Kartoniert (TB), Englisch
- Verlag: Anchor Academic Publishing
- ISBN-10: 3954892839
- ISBN-13: 9783954892839
Sprache:
Englisch
Kommentar zu "Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques"
0 Gebrauchte Artikel zu „Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques".
Kommentar verfassen