Modified Differential Evolution algorithms for Global Optimization

Optimization problems are ubiquitous and consequential. In fact every sphere of human activity that can be quantified can be formulated as an optimization problem. The focus of this work is on Global Optimization which is not only desirable but also necessary in many cases. In the past few decades several Global optimization algorithms have been suggested in literature out of which stochastic, population based search algorithms like Genetic algorithms (GA), Evolutionary Strategies (ES), Swarm Algorithms (Ant Colony (ACO) and Particle Swarm (PSO)), differential Evolution etc. have become immensely popular for solving real life optimization problems. The reason, being the efficiency with which these algorithms can tackle the complex and intricate models of real life problems. This research is concentrated on Differential Evolution which is relatively a newer addition to the population based search algorithms. DE was first suggested by Storn and Price in 1995 as a search technique for solving optimization problems. It uses the same operators like mutation, crossover and selection as that of GA but manipulates them in a manner different to that of GA.