Guest editorial
| DOI | https://doi.org/10.1108/JSIT-11-2018-121 |
| Date | 12 November 2018 |
| Pages | 402-403 |
| Published date | 12 November 2018 |
| Author | Pablo Rabanal,Ismael Rodriguez,Fernando Rubio |
| Subject Matter | Information & knowledge management,Information systems,Information & communications technology |
Guest editorial
Introduction to the special issue on optimisation solutions in systems
Optimisation is a major necessityin science and engineering. No matter if we want to reduce
the amount of needed resources to perform a task or maximise the output of some process,
so often the difficulty of making the right decisions can be rephrased as some kind of
optimisation problem. Unfortunately, for many optimisation problems finding the optimal
solution is not feasible in general due to the hardness of the problem;moreover, for some of
them we cannot even guarantee any constant ratio between the quality of the optimal
solution and the quality of any solution found in reasonable time. Despite these
disheartening theoretical limits, optimisation problems appear whenever there is a
sophisticated system, so we do have to face them by some means –necessarily non-
exhaustive methods. Some of these methodsare specific to the problem under consideration,
whereas others are adaptations of general optimisation heuristics (metaheuristics) to the
studied problem. Typically, the latter search for solutions similar to the most promising
observed ones, or their combinations, for example by making some simple entities interact
with each other according to simple rules and collaboratively construct new solutions.
Within this category we can find evolutionary computation methods and swarm
optimisation methods,which are sometimes inspired by some natural process. Regardlessof
the method selected to tackle a hard optimisationproblem, the difficulty of the problem and
the performance of the best known heuristicsfor the problem may have a high impact on the
application field the problem belongs to, since the difficulty of a scientific or engineering
process can be, to some extent, due to the computational difficulty of the underlying
optimisation problem it implicitly poses. The goal of this special issue is to introduce new
research on optimisationtechniques for engineering systems, and theirapplications.
The special issue received twelve submissions. Amongst them, six papers were selected
for publication. They cover optimisation issues in very different contexts. The first paper,
by Acedo et al., deals with the problem ofinsulin pump therapies in diabetic patients. They
monitored diabetic patients recording data every 5 min, analysing their glucose levels, the
insulin administered and the estimated amount of ingested carbohydrates. To be able to
predict the variation in the glucose levels, a hybrid optimisation technique is used,
combining Particle Swarm Optimisation and Nelder-Mead optimisation. The aim of the
approach is to improve the insulin dosing by fitting to the concrete model parameters of
each patient.
In the second paper, by Dash et al., the optimisationdomain is the design of VLSI circuits.
In this case, the authors use the River Formation Dynamics metaheuristic to minimise the
wire area in large-scale power distribution networks. The usefulness of the approach is
assessed by solving several distributionbenchmarks, including large examples with several
millions of nodes. Observed results are compared against those obtained by using other
well-known methods, like DifferentialEvolution and Particle Swarm Optimisation, showing
that the new approach can outperform them.
In the third paper, by Kaaouache et al., the aim is to optimise the energy consumptionin
Cloud Data Centres.In this case, genetic algorithms are used to assign virtualmachines to as
few energy-efficient physical machines as possible. By minimising used physical machines
(especially during off-peak periods of time), energy can be saved by switching off non-used
physical machines. The paper reports the resultsobtained after simulating the behaviour of
a data centre with 800 heterogeneous physical nodes, testing it with different amounts of
JSIT
20,4
402
Journalof Systems and
InformationTechnology
Vol.20 No. 4, 2018
pp. 402-403
© Emerald Publishing Limited
1328-7265
DOI 10.1108/JSIT-11-2018-121
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