A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing

Abstract

Genetic algorithm is easy to fall into local optimal solution. Simulated annealing algorithm may accept nonoptimal solution at a certain probability to jump out of local optimal solution. On the other hand, lack of communication among genes in MapReduce platform based genetic algorithm, the high-performance distributed computing technologies or platforms can further increase the execution efficiency of these traditional genetic algorithms. To this end, we propose a novel Phoenix++ based new genetic algorithm involving mechanism of simulated annealing. Simulated annealing genetic algorithm has two distinctive characteristics. First, it is the synthesis of the conventional genetic algorithm and the simulated annealing algorithm. This characteristic guarantees our proposed algorithm has a higher probability of getting the global optimal solution than traditional genetic algorithms. The other is that our algorithm is a parallel algorithm running on the high-performance parallel platform Phoenix++ instead of a conventional serial genetic algorithm. Phoenix++ implements the MapReduce programming model that processes and generates large data sets with our parallel, distributed algorithm on a cluster. The experiments indicate that the convergence speed of GA algorithm is significantly faster after adding the simulated annealing algorithm on Phoenix++ platform.