3.2.4. Selection

In DE optimization, the population size is kept constant throughout the generation process. Therefore, a selection criterion provides the best parameter for the next generation. In this process, both parent vector/target vector and the trial vector are compared, and if the trial vector is able to give a better fitness value compared to that of the target vector, then the target vector, i.e., the parent vector, is replaced by the trial vector and the generation gets updated.

DE has its wide acceptance in global search problems. The authors in reference [38] have proposed a DE-based MPPT technique that works with the boost converter for a partially shaded PV system. In the above work, performance of DE algorithm is compared with a conventional algorithm and its efficiency is verified. A detail survey about DE algorithm use in various fields with its advantages and disadvantages is given in reference [43]. The fundamentals of DE, its application to various multi-objective optimization problems, such as constrained, uncertain optimization problems are reviewed in reference [44]. A modified DE with a mutation process being modified, i.e., instead of choosing the parents randomly for mutation, each individual is assigned with a probability of selection, which is inversely proportional to the distance from the mutated individual, is presented in Reference [45]. This modified DE can be applied for solving various optimization problems with some small changes according to the requirement of the optimization problem. A modified DE algorithm for finding the PV model parameters during varying weather conditions and partial shading is given in Reference [46], and the algorithm presented here uses only the PV datasheet information. The original DE and the modified one, and also hybridization of the DE algorithm with various computational techniques or with conventional methods, have been proposed by many researchers [47,48]. The DE algorithm possesses many advantages, but PSO is superior to it in many aspects, such as less coding complexity and parameter tuning.

### *3.3. Ant Colony Optimization (ACO)*

This technique was first proposed by Colorni, Dorigo, and Maniezzo [49]. This is a probability-based algorithm used for a computational problem-solving purpose. This algorithm is inspired by real ants' behavior for searching the shortest path from their colony to an available food source. The ants will follow the shortest distance between their nest and food [15]. Initially, when the ants search for food, they leave a pheromone trail for other ants to follow the same path. This pheromone trail is a chemical material to which members of the same species respond [50]. The thickness of the pheromone trail increases when it is followed by more ants. These ants may also follow the same path while returning to their nest, thereby making the pheromone trail thicker. Hence, the same path is followed by most of the ants till they find any other shortest path by exchanging information about the pheromone. If the path for the food is not the shortest one, then eventually the pheromone disappears [15,27,51].
