Phd Thesis Evolutionary Algorithm

Phd Thesis Evolutionary Algorithm-6
The three dams are located in a consecutive series of Karun River in Iran.

The three dams are located in a consecutive series of Karun River in Iran.

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However, exceptionally, the accuracy of the GA algorithm was approximately 34% better than the HPSOGA algorithm for only the optimal storage capacity at Karun IV Dam.

The overall results show that the optimal values have higher importance in the preparation of the rule curve, especially in periods of drought.

The Genetic Algorithm is an Adaptive Strategy and a Global Optimization technique.

It is an Evolutionary Algorithm and belongs to the broader study of Evolutionary Computation.

This iterative process may result in an improved adaptive-fit between the phenotypes of individuals in a population and the environment.

The objective of the Genetic Algorithm is to maximize the payoff of candidate solutions in the population against a cost function from the problem domain.

The main purpose of this study was presenting a model for an optimal operation of multi-purpose dams of water resources systems.

In this study, a hybrid evolutionary algorithm model (HPSOGA) and linear programming (LP) has been developed for optimizing the operation of reservoirs with the objectives of maximizing hydroelectric power generation, meeting the water demand for agricultural purposes and predicting the cost and estimating amount of agriculture products.

Individuals of a population contribute their genetic material (called the genotype) proportional to their suitability of their expressed genome (called their phenotype) to their environment, in the form of offspring.

The next generation is created through a process of mating that involves recombination of two individuals genomes in the population with the introduction of random copying errors (called mutation).


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