Differential Evolution(DE) has emerged as a powerful and efficient evolutionary algorithm for solving global optimization problems. It adopts the stochastic searching method to make selection of the parents in the mutation operator, which benefits the search of global optimization value. However, the selection method reveals the convergence in low speed. So for the sake of better convergence pe...

A new version of differential evolution (DE) algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution (ISDE), is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algor...

Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. However, the design of its operators makes it unsuitable for many real-life constrained combinatorial optimization problems which operate on binary space. On the other hand, the quantum inspired evolutionary algorithm (QEA) is ver...

This work emphasizes solving the problem of parameter estimation for a human immunodeficiency virus HIV dynamical model by using an improved differential evolution, which is called the hybrid Taguchi-differential evolution HTDE . The HTDE, used to estimate parameters of an HIV dynamical model, can provide robust optimal solutions. In this work, the HTDE approach is effectively applied to solve ...

From within the variety of research that has been devoted to the adaptation of Differential Evolution to the solution of problems dealing with permutation variables, the Geometric Differential Evolution algorithm appears to be a very promising strategy. This approach is based on a geometric interpretation of the evolutionary operators and has been specifically proposed for combinatorial optimiz...

Reentry trajectory optimization is a multi-constraints optimal control problem which is hard to solve. To tackle it, we proposed a new algorithm named CDEN(Constrained Differential Evolution Newton-Raphson Algorithm) based on Differential Evolution(DE) and Newton-Raphson. We transform the infinite dimensional optimal control problem to parameter optimization which is finite dimensional by discr...

The slow convergence and local minima problems associated with neural networks (NN) used for non-linear system identification have been resolved by evolutionary techniques such as differential evolution (DE) combined with Levenberg Marquardt (LM) algorithm. In this work the authors attempted further to employ an opposition based learning in DE, known as opposition based differential evolution (...

In recent years a new evolutionary algorithm for optimization in continuos spaces called Differential Evolution (DE) has developed. DE turns out to need only few evaluation steps to minimize a function. This makes it an interesting candidate for problem domains with high computational costs as for instance in the automatic generation of programs. In this paper a DE-based tree discovering algori...

Differential Evolution (DE) is an evolutionary optimization technique that is exceptionally simple, fast, and robust at numerical optimization. However, the convergence rate of DE in optimizing a computationally expensive objective function still does not meet our requirements, and an attempt to speed up DE is considered necessary. This paper introduces a Modified Differential Evolution (MDE) t...

In various industrial systems, parameters variation is one of the major problems faced by control engineers now days. To overcome the problem of parameter variations, this paper proposes the hybridization of MIT rule based online tuning of classical PID controllers with Differential Evolution algorithm. The hybridization of two techniques results in the offline as well as online tuning of PID c...