Chapter 4 - Stochastic and evolutionary optimization algorithms

Abstract

Stochastic and evolutionary optimization methods are increasingly used to solve challenging optimization problems. These methods are typically inspired by some phenomena from nature and they are robust. These methods are capable of finding the global optimum of multimodal functions and they have flexibility with ease of operation. These algorithms do not require any gradient information and are suitable to solve discrete optimization problems. These methods are extensively used in the analysis, design, and operation of systems that are highly nonlinear, high dimensional and noisy or for solving problems that are not easily dealt by classical deterministic methods of optimization. This chapter mainly focuses on evolutionary and stochastic optimization algorithms such as Genetic algorithm (GA), Simulated annealing (SA), Differential evolution (DE), Ant colony optimization (ACO), Tabu search (TS), Particle Swarm Optimization (PSO), Artificial Bee colony (ABC) algorithm and Cuckoo search (CS) Algorithm. All these methods are discussed with their algorithmic schemes and implementation procedures.