## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Conventional Algorithms (CA)
#### 7.1.2 Meta-Heuristic Algorithms  
Many heuristic algorithms are quite customized and problem specific. A meta-heuristic, on the other hand, is a high-level problem-independent algorithmic framework that offers a set of principles or techniques for the development of heuristic optimization algorithms. However, a specific definition is quite tricky, and many scholars and practitioners use the terms heuristics and meta-heuristics interchangeably. As a result, the term meta-heuristic can also refer to a general-purpose algorithm that is capable of working independently of the specific problem it is applied to, as opposed to a problem-specific implementation of a heuristic optimization algorithm based on the guidelines provided within such a framework. Unlike heuristic methods, a meta-heuristic knows nothing about the problem it will be applied. It can treat functions as black boxes. For more information on meta-heuristic algorithms, the studies in [126] can be examined. In the following paragraphs, we examine meta-heuristic algorithms in three categories. These are algorithms that are derived from the concept of biological evolution. In this regard, the genetic algorithm (GA) is presented as a representative of the evolutionary-based algorithms, as it is one of the most commonly used for energy-efficient UAV-based communications. * Genetic Algorithm: GA is a meta-heuristic optimization method based on the principles of the biological evolution process that finds the best solution to problems that are difficult to solve with exact methods. The first studies on this algorithm were conducted by John Holland. Holland developed new methods for computer systems based on the principles of natural selection and adaptation existing in nature. Holland argued that processes such as crossover, mutation, and selection that take place in the evolutionary process are very important for solving optimization problems and that better individuals can be obtained in each generation. Holland modeled all these processes for solving problems by considering the perfect adaptation of living things in nature to the ecosystem [127]. GA is one of the population-based algorithms used for solving complex problems because it provides a convenient and fast solution. The population consists of individuals that make up the solution set. By eliminating the bad solutions in the solution sets created in each generation, the next generations consist of good solutions that will lead to better results. Since there is more than one solution set in a generation, finding  
Figure 6: Classification of the conventional algorithms discussed within the scope of this work. many best solutions in one step is one of the features that distinguish GA from other algorithms. Additionally, by focusing on the part of the solution set, it can perform an effective search and provide the best solution in a short time [128]. In the GA application process, the first step is to define how to encode the solutions represented by chromosomes according to different problems. After the necessary parameters are received from the user, the initial population is created so that the GA steps can begin. Each chromosome is an individual and consists of genes. The initial population consists of randomly selected chromosomes. Then, the fitness function that defines the problem solution is determined. Afterwards, chromosomes that will form the next generation are selected from the population, and genetic operators based on genetic processes in nature are applied respectively to obtain better chromosomes. The crossover process is applied to generate new offspring from the individuals obtained from the selection process. The mutation tool is used after the crossover step to provide diversification in the population. At the end of all these processes, new generations are created and compared with the fitness values of other generations. Individuals with good fitness are preserved and passed on to other generations (elitism).