Comparison of methods for initializing starting points on the optimization genetic algorithm

The way to initialize the starting points for optimization algorithms is one of the main parameters. Currently used methods of initializing starting points are based on stochastic algorithms of spreading points. In a genetic algorithm, points are Boolean sets. These lines are formed in different ways. They are formed directly, using random sequences (with uniform distribution law) or formed using random sequences (with uniform distribution law) in the space of real numbers, and then converted to boolean numbers. Six algorithms for constructing multidimensional points for global optimization algorithms of boolean sets based on both stochastic and non-random point spreading algorithms are designed. The first four methods of initialization of Boolean lines used a random distribution law, and the fifth and sixth methods of initialization used a non-random method of forming starting points-LP sequence. A large number of optimization algorithms were restarted. Calculations of high accuracy were used. The research was carried out on the genetic algorithm of global optimization. The work is based on Acly function, Rastrigin function, Shekel function, Griewank function and Rosenbrock function. The research was based on three algorithms of srarting points spreading: LP sequence, UDC sequence, regular random spreading. The best parameters of the genetic algorithm of global optimization were used in the work. As a result, we obtained arrays of mathematical expectations and standard deviations of the solution quality for different functions and optimization algorithms. The purpose of the analysis of ways to initialize the starting points for the genetic optimization algorithm was to find the extremum quickly, accurately, cheaply and reliably simultaneously. Methods of initialization were compared with each other by expectation and standard deviation. The quality of the solution is understood as the average error of finding the extremum. The best way of initialization of starting points for genetic optimization algorithm on these test functions is revealed.