## 3 Relevant Research in Wind Farm Design and Optimization
### Solution Methods
#### 3.3.3 Metaheuristic Optimization
The Genetic Algorithm (GA) is the most used population-based metaheuristic in the WFDO problem [159,134,135,156,159,160,161,162,163,165,166,167,168,169,200,207,235,237,245,250,254,278,301,307,353,354,355,356,357]. The GA [327,346] refers to a class of adaptive search procedures based on the principles derived from natural evolution and genetics. In the GA, genes represent the design variables, and potential solutions (_i.e._, individuals) are represented by chromosomes. Like other methods, as the GA proceeds through generations (_i.e._, iterations), it holds a set of possible solutions referred to as the population. Based on the fitness value (_i.e._, the quality of a solution), which is determined through the evaluation of the population's individuals, two individuals (_i.e._, the parents) are selected and are combined through genetic operators (crossover and mutation) resulting in two new individuals (e.g., the offspring). Many different variants of genetic algorithms have been considered in the literature [327] differing in the way genetic operators act over the population and the way individuals are represented. Research has demonstrated that the performance of a genetic algorithm relies upon the proper choice of the selection and crossover mechanisms and the proper design of the individual representation. Another successful population-based metaheuristic algorithm applied to the WFDO problem is the Particle Swarm Optimization (PSO) algorithm [184,192,193,201,203,212,243,264,269,355], which was developed by [PERSON] and [PERSON] in 1995 [345]. The PSO algorithm is inspired by the social behavior of fish schooling and bird flocking. The PSO algorithm relies on the idea in which different paths having different length and complexity can lead to an optimal solution (_i.e._, different particles, or individuals that represent a possible solutions, can take different paths that lead to the same destination, which is assumed to be the place where the performance metric is the best). The PSO algorithm tracks the best position and velocity of each particle integrating the swarm and updates both quantities iteratively. The particles will move in the search space until the stopping criteria is met, which is established when the optimal location has been determined or a given number of iterations have been performed. A very detailed study that contrasts the different SA, GA and PSO implementations used in the WFDO problem is given in [154]. Additional solution procedures based on Ant Colony Optimization [195] and Neural Networks [262] have been used to solve different versions of the WFDO problem. All approaches have shown promising results. However, these solution procedures have been implemented in different scenarios having variable complexity, unknown optimal solutions, substantial differences in main objectives and model constraints, thus limiting any objective conclusion of their effectiveness while solving the WFDO problem. In this regard, a systematic evaluation of solution procedures in realistic wind farm scenarios will provide a basis for the judgment of the performance of each solution procedure in the solution of the WFDO problem. In addition, the performance evaluation of additional heuristics and metaheuristics based on the TABU search, scatter search, memetic algorithms, cuckoo search, _etc._ has not yet been performed.