**1. Introduction**

Several evolutionary algorithms (EAs) have emerged in the past decade that mimic biological entities behavior and evolution. Darwin's theory of evolution is the major inspiration source for EAs. The foundation of Darwin's theory of evolution is natural selection. The study of evolutionary algorithms began in the 1960s. Several researchers independently developed three mainstream evolutionary algorithms, namely, genetic algorithms [1, 2], evolutionary programming [3], and evolution strategies [4]. EAs are widely used for the solution of single and multi-objective optimization problems. Swarm Intelligence (SI) algorithms are also a special type of EAs. SI can be defined as the collective behavior of decentralized and selforganized swarms. SI algorithms among others include Particle Swarm Optimization (PSO) [5], Ant Colony Optimization [6], and Artificial Bee Colony (ABC) [7].

PSO is an evolutionary algorithm that mimics the swarm behavior of bird flocking and fish schooling [5]. The most common PSO algorithms include the classical Inertia Weight PSO (IWPSO) and Constriction Factor PSO (CFPSO) [8]. PSO is an easy to implement algorithm with computational efficiency. The PSO algorithm is inherently used only for real-valued problems. An option to expand PSO for discrete valued problems also exists. Among others PSO algorithms include, the barebones (BB) and the exploiting barebones (BBExp). BBPSO has been successfully applied to the cell to switch assignment problem [9].

Artificial Bee Colony (ABC) [7] is a recently proposed SI algorithm, which has been applied to several real world engineering problems. The ABC algorithm models and simulates the honey bee behavior in food foraging. In the ABC algorithm, a potential solution to the optimization problem is represented by the position of a food source while the food source corresponds to the quality (objective function fitness) of the associated solution. The ABC algorithm has been

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successfully applied to several problems in wireless communications [10]. ABC variants that improve the original algorithm have also been proposed [11].

Ant Colony Optimization (ACO) is a population-based metaheuristic introduced by Marco Dorigo [12]. This algorithm was inspired by the behaviour of real ants. The algorithm is based on the fact that ant colonies can find the shortest path between their nest and a food source just by depositing and reacting to pheromones while they are exploring their environment. ACO is suitable for solving combinatorial optimization problems, which are common in wireless communications.

Differential evolution (DE) [13, 14] is a population-based stochastic global optimization algorithm, which has been used in several real world engineering problems. Several DE variants or strategies exist. One of the DE advantages is that very few control parameters have to be adjusted in each algorithm run. However, the control parameters involved in DE are highly dependent on the optimization problem. Moreover, the selection of the appropriate strategy for trial vector generation requires additional computational time using a trial-anderror search procedure. Therefore, it is not always an easy task to fine-tune the control parameters and strategy. Since finding the suitable control parameter values and strategy in such a way is often very time-consuming, there has been an increasing interest among researchers in designing new adaptive and self-adaptive DE variants. Self adaptive DE (SaDE), is a DE algorithm that self-adapts both control parameters and strategy based on learning experiences from previous generations is presented in [15-17]. SaDE has been applied to microwave filter design, [18], and to antenna arrays synthesis [19].

The purpose of this chapter is to briefly describe the above algorithms and present their application to wireless communications optimization problems found in the literature. This chapter also presents results from different cases using PSO, ABC, ACO and DE. These include the cell to switch assignment problem in cellular networks using PSO algorithms, peak to average power ratio (PAPR) reduction of OFDM signals with the partial transmit sequences (PTS) approach using ABC and ACO algorithms [7, 11], and dual-band microwave filter design for wireless communications using SADE.

This chapter is subdivided into four sections. Section 2 presents the different evolutionary algorithms. Section 3 reviews the related work in wireless communications problems from the literature. Section 4 describes the design cases and presents the numerical results. Finally section 5 contains the discussion about the advantages of using a EA-based approach and the conclusions.
