A Problem Solving Environment for Combinatorial Optimization Based on Parallel Meta-heuristics

Abstract

Computational grid offers a great potential solution to parallel meta-heuristics toward combinatorial optimization. However, it is quite difficult for specialists in combinatorial optimization to develop parallel meta-heuristics in extremely heterogeneous computational environment, starting from scratch without any toolkit. This paper presents a problem solving environment for combinatorial optimization based on parallel meta-heuristics (PSEPMH) to help specialists to harness heterogeneous computational resources and handle dynamic granularity control. PSEPMH requires specialist to decompose one problem into two sub-problems with divide-and-conquer framework just as generic sequential algorithm. Then compiler of PSEPMH generates mobile agent code that automatically forms adaptive multi-granularity parallel computing at runtime by cloning himself and distributing along dynamic, complex grid environment with the support of PSEPMH. Not only can PSEPMH relieve specialists' burden, but also make use of the computational resources more efficiently.