4.2.1. Genetic Algorithm

Genetic algorithm (GA) is an intelligence search algorithm that was introduced earlier to solve optimization problems. GA is developed from natural selection and genetics principles such as selection, mutation, inheritance and crossover [56,57]. In GA, a set of selection rules is specified to allow a population to achieve a maximum state of fitness. Then, the elements in a population are integrated into chromosomes to enable the potential elements to achieve a better state. The first population of elements evolved through the evolution of generations. The principle of mutation is applied to modify the chosen element to evolve into a new population. The algorithm repeats this procedures until an acceptable solution or the highest number of iterations is attained. [4,6,56]. Genetic algorithms utilize continuous and discrete variables for implementation and work better at obtaining global optimums of various functions. GAs can effectively solve poorly defined and complex problems. GA is the most used optimization method to find optimal locations and sizes of DGs in the literature [22,81,82]. In Liu et al. [22], the authors presented a mixed-integer GA to obtain optimal sizes and locations of hybrid battery energy storage and renewable energy DGs units with objective aiming to minimise system total cost, enduser satisfaction loss caused by demand side management, and tie-line power fluctuation. The methodology in Liu et al. effectively determined the solution of the multi-objective optimization problem compared to others validated with it. However, neither uncertainties of the renewable energy sources nor the voltage variability of the BESS were modelled. In addition, the requirements for the evaluation of network stability and harmonic contents were not included in the proposed methodology. Moreover, genetic algorithms have the disadvantage of evaluating the repeated fitness functions that are time intensive for large and complex problems. The various configurations of GA that are proposed to improve the performance of the GA method in the DG allocation problems are quantum GA (QGA) [83], adaptive genetic algorithm (AGA) [84], etc.
