Cognitive modeling on high performance computing platforms is an emerging field. A preliminary analysis i s presented on the use of parallel processing and genetic algorithms for optimizing the fit of non-linear, multivariable symbolic models of human cognition to experimental data. The effectiveness of this experimental optimization methodology i s illustrated with a prototype model of a seria...

A practical online optimization scheme is developed for performance optimization of an electrical aircraft propulsion system. The goal is to minimize the power extraction of the propulsion system for any given thrust value. The online optimizer computes the optimum pitch angle of a variable pitch propeller by minimizing the power of the system for a command thrust value. This algorithm is teste...

In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...

In this paper, a general framework for image contrast enhancement algorithm based on an optimization problem is presented. Through this optimization, the intensities can be better distributed. The algorithm is based on the facts that the histogram of the enhanced image is close to the input image histogram and uniform distribution, simultaneously. Based on this fact, we obtain a closed form opt...

In the current work, different methods for optimization of turbine blade internal cooling are investigated, to achieve higher cyclic efficiency and output power for a typical gas turbine. A simple two-dimensional model of C3X blade is simulated and validated with available experimental data. The optimization process is performed on this model with two different methods. The first method is ...

Many scientific and engineering applications involve finding more than one optimum. A comprehensive review of the existing works done in the field of multimodal function optimization was given and a critical analysis of the existing methods was also provided. Several techniques in solving multimodal function optimization problems were introduced, such as clearing, deterministic crowding, sharin...

In this paper, a new algorithm which is the result of the combination of cellular learning automata and frog leap algorithm (SFLA) is proposed for optimization in continuous, static environments.At the proposed algorithm, each memeplex of frogs is placed in a cell of cellular learning automata. Learning automata in each cell acts as the brain of memeplex, and will determine the strategy of moti...