Optimization of parallel manipulators using genetic algorithms

The Multidisciplinary Design Optimization of a Reentry Vehicle Using Parallel Genetic Algorithms

The purpose of this paper is to examine the multidisciplinary design optimization (MDO) of a reentry vehicle. In this paper, optimization of a RV based on, minimization of heat flux integral and minimization of axial force coefficient integral and maximization of static margin integral along reentry trajectory is carried out. The classic optimization methods are not applicable here due to the c...

Link Mass Optimization Using Genetic Algorithms for Industrial Robot Manipulators

Obtaining optimum energy performance is the primary design concern of any mechanical system, such as ground vehicles, gear trains, high speed electromechanical devices and especially industrial robot manipulators. The optimum energy performance of an industrial robot manipulator based on the minimum energy consumption in its joints is required for developing of optimum control algorithms (Delin...

Parallel Heterogeneous Genetic Algorithms for Continuous Optimization

In this paper we address the physical parallelization of a very efficient genetic algorithm (GA) known as gradual distributed real-coded GA (GD-RCGA). This search model naturally provides a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimi...

automatic verification of authentication protocols using genetic programming

implicit and unobserved errors and vulnerabilities issues usually arise in cryptographic protocols and especially in authentication protocols. this may enable an attacker to make serious damages to the desired system, such as having the access to or changing secret documents, interfering in bank transactions, having access to users’ accounts, or may be having the control all over the syste...

A dexterity comparison for 3-DOF planar parallel manipulators with two kinematic chains using genetic algorithms