In the last decade, many heuristic methods have evolved for solving optimization problems that were previously difficult or impossible to solve. These methods include simulated annealing (SA), tabu search (TS), genetic algorithm (GA), differential evolution (DE), evolutionary programming (EP), evolutionary strategy (ES), ant colony optimization (ACO), and particle swarm optimization (PSO). This...

A cultural algorithm is proposed in this paper. The main novel feature of this approach is the use of differential evolution as a population space. Differential evolution has been found to be very effective when dealing with real valued optimization problems. The knowledge sources contained in the belief space of the cultural algorithm are specifically designed according to the differential evo...

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaini...

In this paper we present the design and performance analysis of a Proportional Integral Derivate (PID) controller for the Autonomous Power system model using Differential Evolutionary Algorithm (DEA) for optimization of different parameter that can be tuned to improve the performance of the system studied. Differential evolutionary algorithm is a branch of evolutionary algorithm which is quiet ...

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, 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 a simple and modified hybridized Differential Evolution algorithm for solving global optimizat...

The R package DEoptim implements the Differential Evolution algorithm. This algorithm is an evolutionary technique similar to classic genetic algorithms that is useful for the solution of global optimization problems. In this note we provide an introduction to the package and demonstrate its utility for financial applications by solving a non-convex portfolio optimization problem.

The application of Evolutionary algorithms (EAs) for multi-objective problems (MOPs) has been receiving a growing interest since 1980s. Among the large amount of literature in this area, differential evolution (DE) for MOPs is widely investigated and has shown several advantages over other EAs. However, due to the inherent characteristics of MOPs, plain DE cannot be directly applied for MOPs bu...

The detection of voltage collapse is essential to avoid possible voltage collapse for the preventive control actions and voltage security assessment. One effective way to know the locations where voltage collapses could be appear is to identify weakest buses in the systems. The weakest bus is the first point where voltage collapses appear in a severe contingency. This paper proposes a technique...

Artificial neural networks provide a methodology for solving many types of nonlinear problems that are difficult to solve using traditional techniques. Neurogenetic hybrid systems bring together the artificial neural networks benefits and the inherent advantages of evolutionary algorithms. A functional approximation method using neuro-genetic hybrid systems is proposed in this paper. Three evol...