Application of stochastic and evolutionary optimization algorithms to base case problems

Abstract Stochastic and evolutionary optimization methods are more and more used in the analysis, design, and operation of systems that are highly nonlinear, high dimensional, and noisy or for problems that are not easily solved by classical deterministic methods of optimization. This chapter primarily concentrates on evaluating the results of various stochastic and evolutionary optimization algorithms such as genetic algorithm simulated annealing, differential evolution, ant colony optimization, particle swarm optimization, and artificial bee colony algorithm by applying them to different base case problems. Three typical base case problems of different complexities are used for implementation and evaluation of these methods. For the comparative performance, these methods are studied in terms of different criteria such as speed of convergence, number of iterations required to obtain the final converged solution, and complexity of model equation. The solution strategies and the analysis of results on application to base case nonlinear optimization problems of this chapter lead to extend the usefulness of these methods to solve real and complex optimization problems of different domains.