Phd Thesis Evolutionary Algorithm

Phd Thesis Evolutionary Algorithm-45
The strategy for the Genetic Algorithm is to repeatedly employ surrogates for the recombination and mutation genetic mechanisms on the population of candidate solutions, where the cost function (also known as objective or fitness function) applied to a decoded representation of a candidate governs the probabilistic contributions a given candidate solution can make to the subsequent generation of candidate solutions.Algorithm (below) provides a pseudocode listing of the Genetic Algorithm for minimizing a cost function.The amount of water allocated to the mentioned land was about 6.240 MCM.

Tags: Argumentative Research Paper ThesisBusiness Plan Forecast TemplateHamlet Madness EssayShort Essay About History Of ComputerScience Research Articles On HealthCommercial Ad Analysis Essay

An improved particle swarm algorithm (HPSOGA) is used to solve complex problems of water resources optimization.

One of the main problems of this method is premature convergence and to improve this problem, the compound of the particle swarm algorithm and genetic algorithm were evaluated.

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.

Then, the optimal output of the problem in the form of curves that represent the desired amount of discharge from the reservoir at a specified time interval were prepared and compared with the Lingo model.

The regression analysis and artificial neural networks (ANN) were used to check the quality of the results.The three dams are located in a consecutive series of Karun River in Iran.In order to optimize, 41 years of the common statistical period were used.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.To achieve the best cultivation pattern, initially the arable land was categorized into 6 classes and only 2100 hectares of agricultural irrigable land that had the best agricultural conditions were studied.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.The described model was resolved by linear programming and evolutionary algorithms in Microsoft Excel (Solver).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.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.

SHOW COMMENTS

Comments Phd Thesis Evolutionary Algorithm