A MapReduce Enabled Simulated Annealing Genetic Algorithm

Intelligent algorithms such as genetic algorithms and simulated annealing algorithms have widely been applied to the field of large scale data analysis and data processing. It is potential for the high-performance distributed computing technologies or platforms to further increase the execution efficiency of these traditional intelligent algorithms. Against this background, we propose a novel MapReduce enabled simulated annealing genetic algorithm that has two distinctive characteristics. The first is that, our algorithm is the synthesis of the conventional genetic algorithm and the simulated annealing algorithm. While most genetic algorithms are easy to fall into local optimal solution, the simulated annealing algorithm accepts non-optimal solution at a certain probability to jump out of local optimal. 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++ other than 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 on Phoenix++ indicate that the convergence speed of the proposed algorithm significantly outperforms its traditional genetic rivals.

1. Introduction

Intelligent algorithms such as genetic algorithms and simulated annealing algorithms have widely been applied to the field of large scale data analysis and data processing [1]. For example, we can easily find the implementation of the genetic algorithms in the fields of combinatorial optimization, machine learning, signal processing, adaptive control, bioinformatics and big data processing [2].