|Article title||PARALLEL POPULATION ALGORITHM|
|Authors||D. Ju. Zaporozhets, D. V. Zaruba|
|Section||SECTION III. EVOLUTIONARY MODELING AND BIOINSPIRED ALGORITHMS|
|Month, Year||04, 2018 @en|
|Abstract||The architecture of parallel search is proposed herein. The following three levels of parallelization are distinguished: microlevel, that is the parallelization of tasks at the level of execution of operators generating new alternative solutions; macrolevel, that is the parallelization of the search at the generation level; meta-level, that is the parallelization of the search process at the level of an isolated set of alternative solutions. As an example, herein we propose the implementation of a parallel genetic algorithm with parallelization at the microlevel. A generalized architecture is proposed and the main steps of a new parallel genetic algorithm with parallelization at the micro level are described. The basic idea of this approach is the initialization of N threads, in each of which the crossing-over operator is executed and, with a certain probability, mutation and inversion operators. For all new solutions, the fitness is calculated in the flow in which the decision was received. The algorithm waits for all threads to end. Then, all new generated solutions are combined into one population and the selection operator is executed. The process continues iteratively after reaching the specified number of iterations. To confirm the effectiveness of the proposed approach, a software implementation of the parallel genetic algorithm was developed. As an applied problem one of the classical optimization problems was chosen - the Hamiltonian cycle in the graph. The purpose of conducting experimental studies is the speed of the implemented genetic algorithm, as well as its comparison with the classical "sequential" genetic algorithm. The research was conducted on two different platforms with processors: Intel I7 – 4 cores, 2.7 GHz and AMD 6000 – 2 2.5 GHz cores. Experimental studies have been performed that showed a significant increase in the speed of the algorithm with a number of vertices in the graph of more than 250. The speed of the algorithm increased by 100–200 % compared with the classical implementation, depending on the processor used. Thus, it has been experimentally proved that with the use of the same hardware, the proposed parallel approach makes it possible to shorten the running time of the optimization algorithm by several times compared to its classical sequential implementation.|
|Keywords||Genetic algorithms; parallel search; population algorithms; traveling salesman problem; hamiltonian cycle.|
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