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Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques

Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques

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Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques

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Beschreibung

Details

Einband

Taschenbuch

Erscheinungsdatum

27.05.2014

Verlag

Anchor Academic Publishing

Seitenzahl

62

Beschreibung

Details

Einband

Taschenbuch

Erscheinungsdatum

27.05.2014

Verlag

Anchor Academic Publishing

Seitenzahl

62

Maße (L/B/H)

22/15,5/0,6 cm

Gewicht

123 g

Sprache

Englisch

ISBN

978-3-95489-283-9

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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 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
  • Particle swarm optimizer: Economic dispatch with valve point effect using various PSO techniques