On the Application of the Cascade Optimization Algorithm in Distributed Computer Networks and Grids

On the application of the Cascade Optimization Algorithm in distributed computer networks and grids

Franjo Cecelja^a , Antonis Kokossis^b, Du Du^a,

^aPRISE, FEPS, Unverisity of Surrey, Guildford, Surrey, GU2 7UB, U.K., f.cecelja@surrey.ac.uk

^bSchool of Engineering, National Technical University of Athens, Zofrafou Campus, 9, GR-15780, Athens, Greece, akokossis@mail.ntua.gr

  Abstract

The paper builds on the Cascade Optimization Algorithm (COA) a recent approach with a strong potential to tackle complex problems using modular structures (pools). Figure 1 illustrates the pool structure, the coordination task, as well as flows of calculations in the course of optimization. COA deploys Markov processes by means of peer pools populated by clients propagating the Markov chains, and breaking down (decomposing) peer-to-peer communication through a (purely) parallel task (coordination task). Without any burden to the optimization, sophistication can be added to this task customizing the selection of chains and upgrading intermediate data into information and knowledge. Dotted lines in Figure 1 highlight exploitable information links with the coordination layer.

Figure 1

Using the distributable components of the Cascade Optimization Algorithm the paper presents evidence on the potential of the algorithm to couple with computer grids. Comparisons are drawn with the standalone algorithm as well as Tabu Search - an algorithm comparatively easy to implement on the grids - and does report noticeable improvements in computational performance. The algorithm is further tested on problems of industrial complexity as is the synthesis of chemical reactor networks offering evidence on biocatalytic reactions of several components and reaction mechanisms.

The paper further scopes for improvements in the cascading stages, also means to include self-supervised stages. As anticipated, the results show that the execution time to convergence reduces with increasing the number of CPUs with faster CPUs contributing more than slower ones. As for the self-supervised system, search directions have been intentionally biased by putting different weights on different patterns of intrinsic parameters for each solution according to on-time analytical results of these parameters. Results show that the optimization search converges more quickly by applying more parameters in the production rules in the model.

In conclusion, grid implementation of stochastic optimisation algorithm generally improves the performance in terms of execution time. Bringing in intelligent self-supervised and knowledge-based optimization, it is possible to improve the optimization performance, in particular convergence speed.  

Kokossis, A., P. Linke and S. Yang, The Cascade Optimization Algorithm:  A New Distributed Approach for the Stochastic Optimization of Engineering Applications, Ind. Eng. Chem. Res., 2011, 50 (9), pp 5266–5278