Performance Comparison between Simple and Adam—Eve-Like Genetic Algorithms in Optimal PMU Placement Problem

In this research, the Adam-Eve algorithm is investigated, and its performance is compared with the simple genetic algorithm. The simple genetic algorithm is the simplest type of genetic algorithm in which the population size is constant, and the probability of mutation and crossover in it does not change over different generations. Adam-Eve algorithm is one of the adaptive algorithms that the number of population and rates of mutation and crossover in it are variable. In this research, both algorithms are implemented, and they are used to solve the phasor measurement unit placement problem in the power system. The simulation results show that the probability of achieving global optimal in the Adam-Eve algorithm is higher, although the convergence speed of this algorithm decreases.