In this paper, a chaos search immune algorithm (CSIA) is proposed by integrating the chaos optimization algorithm and the clonal selection algorithm. First, optimization variables are expressed by chaotic variables through solution space transformation. Then, taking advantages of the ergodic and stochastic properties of chaotic variables, a chaos search is performed in the neighbourhoods of hig...

This work addresses the signal propagation and the fractional-order dynamics during, the evolution of a genetic algorithm (GA), for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitati...

in this paper, a method based on binary-coded genetic algorithm is proposed to explore an optimization method, for obtaining an optimal elliptical tank. this optimization method enhances the rollover threshold of a tank vehicle, especially under partial filling conditions. minimizing the overturning moment imposed on the vehicle due to c.g. height of the liquid load, lateral acceleration and ca...

this paper proposes an optimized control policy over type one diabetes. type one diabetes is taken into consideration as a nonlinear model (augmented minimal model), which is implemented in matlab-simulink. this model is developed in consideration of the patient's conditions. there are some uncertainties in the regarded model due to factors such as blood glucose concentration, daily meals ...

The shielding design is one of the most difficult phases in developing an inductive power transfer system (IPT) for electric vehicles. In this aspect, combination metamodeling with a multiobjective optimization algorithm provides efficient approach. Here, Polynomial Chaos Expansions (PCE) and Multigene Genetic Programming Algorithm (MGPA) methods are used compared to describe mutual inductance ...

today, with the advanced statistical techniques and neural networks, predictive models of distribution have been rapidly developed in ecology. purpose of this research is to predict and map the distribution of tetranychus urticae koch (acari: tetranychidae) using mlp neural networks combined with genetic algorithm in surface of farm. population data of pest was obtained in 2016 by sampling in 1...

the mathematical modeling of fracture networks is critical for the exploration and development ofnatural resources. fractures can help the production of petroleum, water, and geothermal energy. theyalso greatly influence the drainage and production of methane gas from coal beds. orientation andspatial distribution of fractures in rocks are important factors in controlling fluid flow. the object...