In this paper we propose an efficient offline job scheduling algorithm working in a grid environment that is based on a relatively new evolutionary metaheuristic called generalized extremal optimization (GEO). We compare our experimental results with those obtained using a very popular evolutionary metaheuristic, the genetic algorithm (GA). The scheduling algorithm implies two-stage scheduling. In the first stage, the algorithm allocates jobs to suitable machines of a grid; GEO/GA is used for this purpose. In the second stage, jobs are independently scheduled on each machine; this is performed with a variant of a list scheduling algorithm. Both GEO and GA belong to the class of evolutionary algorithms, but GEO is much simpler and requires the tuning of only one parameter, whereas GA requires the tuning of several parameters. The results of the experimental study show that GEO, despite its simplicity, outperforms the GA in a whole range of scheduling instances and is much easier to use.

The paper deals with the concrete planning problem -a stage of the web service composition in the PlanICS framework. We present several known and new methods of concrete planning including those based on Satisfiability Modulo Theories (SMT), Genetic Algorithm (GA), as well as methods combining SMT with GA and other nature-inspired algorithms such as Simulated Annealing (SA) and Generalised Extremal Optimization (GEO). The discussion of all the approaches is supported by the complexity analysis, extensive experimental results, and illustrated by a running example.

We present nine SAT-solvers and compare their efficiency for several decision and combinatorial problems: three classical NP-complete problems of the graph theory, bounded Post correspondence problem (BPCP), extended string correction problem (ESCP), two popular chess problems, PSPACE-complete verification of UML systems, and the Towers of Hanoi (ToH) of exponential solutions. In addition to several known reductions to SAT for the problems of graph k-colouring, vertex k-cover, Hamiltonian path, and verification of UML systems, we also define new original reductions for the N-queens problem, the knight's tour problem, and ToH, SCP, and BPCP. Our extensive experimental results allow for drawing quite interesting conclusions on efficiency and applicability of SAT-solvers to different problems: they behave quite efficiently for NP-complete and harder problems but they are by far inferior to tailored algorithms for specific problems of lower complexity.

No abstract