Exploring dynamic self-adaptive populations in differential evolution

Tuning of Extended Kalman Filter using Self-adaptive Differential Evolution Algorithm for Sensorless Permanent Magnet Synchronous Motor Drive

In this paper, a novel method based on a combination of Extended Kalman Filter (EKF) with Self-adaptive Differential Evolution (SaDE) algorithm to estimate rotor position, speed and machine states for a Permanent Magnet Synchronous Motor (PMSM) is proposed. In the proposed method, as a first step SaDE algorithm is used to tune the noise covariance matrices of state noise and measurement noise i...

Developing Adaptive Differential Evolution as a New Evolutionary Algorithm, Application in Optimization of Chemical Processes

A Differential Evolution and Spatial Distribution based Local Search for Training Fuzzy Wavelet Neural Network

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima

Despite the fact that evolutionary algorithms often solve static problems successfully, dynamic optimization problems tend to pose a challenge to evolutionary algorithms [21]. Differential evolution (DE) is one of the evolutionary algorithms that does not scale well to dynamic environments due to lack of diversity [35]. A significant body of work exists on algorithms for optimizing dynamic prob...

An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization

Differential evolution (DE) is a simple, fast, and efficient evolutionary algorithm for global numerical optimization. The major advantage of DE is its self-adaptation in both search direction and step size of the differential mutation.. On the other hand, DE is good at exploring. The search space and locating the region of global minimum, but it is slow at exploitation of the solution. In orde...