Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to u...

Digital cameras to reduce cost, use an image sensor to capture color images. Color Filter Array (CFA) in digital cameras permits only one of the three primary (red-green-blue) colors to be sensed in a pixel and interpolates the two missing components through a method named demosaicking. Captured data is interpolated into a full color image and compressed in applications. Color interpolation bef...

This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multiobjective optimization problem where parameters are interdependent. The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulti...

Differential Evolution (DE) has been applied to many scientific and engineering problems for its simplicity and efficiency. However, the standard DE cannot be used in a binary search space directly. This paper proposes an adaptive binary Differential Evolution algorithm, or ABDE, that has a similar framework as the standard DE but with an improved binary mutation strategy in which the best indi...

Differential Evolution (DE) is a novel evolutionary approach capable of handling nondifferentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents an Improved Constraint Differential Evolution (ICDE) algorithm for solving constrained optimiza...

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...

In this paper, use of Differential Evolution (DE) based and Particle swarm optimization (PSO) based algorithm for the allocation & coordinated operation of multiple FACTS (Flexible AC Transmission System) devices for the improved power transfer capacity and economic operation of an interconnected power system is presented. Both the DE and PSO based approach is applied on IEEE 30-bus system. The...

Created in 1994, Differential Evolution (DE) is relatively new but already has been proven to perform well for optimisation in static environments. But with many realworld optimisation problems having non-static environments, how well can DE perform in these situations? What changes can be done to allow DE to perform better in dynamic environments?

Differential evolution (DE) algorithm is used to design a microstrip antenna, loaded with a shorting pin. The position of probe and the position of shorting pin are optimized using DE. The fitness function for DE is obtained using multiport network modelling technique. Antenna is fabricated and measured results are compared with the theoretical results.

Differential Evolution (DE) is a powerful metaheuristic method used to numerically optimize functions or multidimensional problems not solved by traditional methods of global optimization. In turn, if boundary conditions are added, constraint handling techniques should be need. To improve DE’s performance, algorithms of local search are a good alternative. Hybridization of Differential Evolutio...