Back propagation neural network is successfully used in various fields, particularly in pattern recognition. Despite numerous applications, back propagation neural network`s design and optimization are developed by trial-and-error process, which is time-consuming. On the other hand, although a dataset may contain many features, these features may not be useful in a back propagation neural netwo...

This paper introduces a stochastic evolutionary algorithm known as hybrid Differential Evolution (DE) for the design of digital High pass FIR filter. It combines the features of both the basic DE and exploratory move for the fine tuning of DE parameters locally as well as globally in the promising search area. A multi-objective function having different performance requirements of minimum magni...

ABSTRACT In many chemical engineering process control applications, one frequently encounters nonlinear optimal control problems. The solution of these types of problems is usually very difficult due to their highly nonlinear, multidimensional and multimodal nature. Several deterministic techniques have been proposed to solve these problems but they are computationally expensive and more likely...

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Multi-objective Optimization Problems (MOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous ...

The use of evolutionary strategies (ESs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential Evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. Th...

Differential evolution algorithm (DE) is one of the novel stochastic optimization methods. It has a better performance in the problem of the color image quantization, but it is difficult to set the parameters of DE for users. This paper proposes a color image quantization algorithm based on self-adaptive DE. In the proposed algorithm, a self-adaptive mechanic is used to automatically adjust the...

Differential evolution is a powerful evolution algorithm for optimization of real valued and multimodal functions. To accelerate its convergence rate and enhance its performance, this paper introduces a top-p-best trigonometric mutation strategy and a self-adaptation method for controlling the crossover rate ( CR ). The performance of the proposed algorithm is investigated on a comprehensive se...

This paper proposes an efficient differential evolution (DE) algorithm for the solution of the optimal reactive power dispatch (ORPD) problem. The main objective of ORPD is to minimize the total active power loss with optimal setting of control variables. The continuous control variables are generator bus voltage magnitudes. The discrete control variables are transformer tap settings and reacti...

Differential evolution (DE) is a powerful and simple algorithm for singleand multi-objective optimization. However, its performance is highly dependent on the right choice of parameters. To mitigate this problem, mechanisms have been developed to automatically control the parameters during the algorithm run. These mechanisms are usually a part of a unified DE algorithm, which makes it difficult...