Phd Thesis Evolutionary Algorithm Ghost Dances Themes Essay

Posted by / 01-Dec-2020 11:07

Comparing the results of the combination of the PSO and GA algorithms makes clear that the obtained algorithm increased flexibility and improving the ability of the PSO algorithm to create the population with high-speed convergence and it is very applicable to solve the problems of operation optimization of water resources.

To compare the accuracy of the results, three criteria were used for RMSE, NRMSD and CV. optimum release, optimum storage and the produced energy, for all dams, the accuracy of HPSOGA was better than GA and GA accuracy was remarkably better than PSO.

The basis of this compound is in such a way that the advantages of the Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) have been applied simultaneously.

Two efficient operators of Genetic Algorithm, that is, mutation and crossover are used in the obtained algorithm, the mutation causes an increase in the diversity of the population and the intersection of information between the particles of the population.

To evaluate the hybrid algorithm, optimization of hydro-power energy of Karun dams were considered.

Cases studied in this research were reservoirs of Karun I, Karun III and Karun IV.

By using the Weibull distribution, the base year which is consistent with the percent probability of agricultural needs was determined for downstream of the Karun III dam.

The regression analysis and artificial neural networks (ANN) were used to check the quality of the results.The results showed full compliance of these two methods.To estimate and predict the cost of the different stages of farming, and the cost of fertilizers needed for agricultural products, the obtained results of cultivation pattern per acre multiplied to cost breakdown values in tables taken from the ministry of agriculture.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.

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The optimization problem was modelled with the aim of maximizing the ultimate value of agriculture in terms of the number of acres of each crop.

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