Soft Computing

, Volume 21, Issue 17, pp 4843–4844 | Cite as

Special issue IDC 2015 & INISTA 2015

  • Cesar AnalideEmail author
  • Jason Jung

1 Introduction

The eight papers included in this special issue represent extended versions of a selection of the best contributions presented at the \(9{\mathrm{th}}\) International Symposium on INnovations in Intelligent SysTems and Applications (INISTA’2015), held in Madrid, Spain, in September, and organized by the Applied Intelligence & Data Analysis (AIDA) group, Universidad Autónoma de Madrid, Spain, together with the Yildiz Technical University, Republic of Turkey, and, also, by extended versions of a selection of the best contributions presented at the \(9{\mathrm{th}}\) International Symposium on Intelligent Distributed Computing (IDC’2015), held in Guimarães, Portugal, in October, and organized by the Intelligent Systems Lab and ALGORITMI Centre, University of Minho, Portugal.

This special issue is aimed at practitioners, researchers, and postgraduate students who are engaged in developing and applying intelligent distributed solutions to problems together with innovative applications with intelligent systems to solve real-world problems.

2 Contributions on this special issue

The papers are organized as follows.

The contribution by R. Mallol-Poyato et al. proposes a novel adaptive nesting evolutionary algorithm to jointly optimize two important aspects of the configuration and planning of a microgrid: the structure’s design and the way it is operated in time.

Helge Spieker et al. proposes that maximal covering location problems have efficiently been solved using evolutionary computation. The multistage placement of charging stations for electric cars is an instance of this problem. It is particularly challenging, because a final solution is constructed in multiple steps, stations cannot be relocated easily and intermediate solutions should be optimal with respect to certain objectives.

Ana-Maria Nogared and David Camacho states that both resource allocation and the timetabling problems can be found in universities where students can select courses they would like to attend before or after the timetabling is done. When demand exceeds capacity, the universities may allocate the available seats independently from the timetabling, but students may have then to decide which courses they are going to attend because of clashes in their timetable. In addition to that, students may submit preferences over courses, and the school administration has to assign seats and do the timetable considering both preferences and clashes. In this contribution, both problems, seats allocation and timetabling, have been modelled separately and combined as constraint satisfaction optimization problems (CSOP)

Cristian Ramirez-Atencia et al. points out that, due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Mission planning for UAVs is the process of planning the locations and actions for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This contribution presents a new multiobjective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs.

Evelia Lizárraga et al. consider a NP-hard combinatorial optimization problem from computational biology where, given a set of input strings of equal length, the goal is to identify a maximum cardinality subset of strings that differ maximally in a predefined number of positions. A comprehensive experimental comparison among proposed techniques shows, first, that larger neighbourhood search generally outperforms both greedy strategies and, second, while large neighbourhood search shows to be competitive with the stand-alone application of CPLEX for small- and medium-sized problem instances, it outperforms CPLEX in the context of larger instances.

Davide Carneiro et al. states that the number of jobs that takes place in a computer is very significant. These workplaces often offer the key ingredients for the emergence of stress and the performance drop of its long-term effects. This has a toll on productivity and work quality, with significant costs for both organizations and workers. This work contributes to the current need for the development of non-intrusive methods for monitoring and managing worker performance in real time. A framework is proposed which assesses worker performance and a case study in which this approach was validated.

Amelia Bădică and Contin Bădică suggest that swarm computing is an emerging computing paradigm suitable for solving difficult optimization problems by employing a nature-inspired search for solutions. Swarm entities are modelled as computational objects that follow a specific set of behavioural laws of natural inspiration. The main research result reported in this contribution is the proposal of a new formal computational model of a generic distributed framework for swarm computing. This model captures the basic computational properties of the swarm using the formal language of finite state process algebra. The proposed model is simple, clear and abstract.

Cristina Perfecto et al. implies that almost all contributions within the research area of its contribution focus on the use of standard crossover and mutation operators from genetic algorithms onto the graph topology beneath encoded individuals. This manuscript elaborates on the topological heritability of the so-called Dandelion tree encoding approach under non-conventional operators.



This special issue has been achieved by a number of fruitful collaborations. Thus, the guest editors wish to thank Prof. Antonio Di Nola and Prof. Vincenzo Loia, (Editor-in-Chief and Co-Editor-in-Chief of the Soft Computing Journal) for providing the opportunity to edit this special issue. We would also like to thank the referees who have critically evaluated the papers within the short time and the editorial staff for their support. Finally, we hope the reader will share our joy and find this special issue very useful for their research or applications area.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Departamento de Informática/Centro ALGORITMI, Escola de EngenhariaUniversidade do MinhoBragaPortugal
  2. 2.Knowledge Engineering Laboratory, Department of Computer EngineeringChung-Ang UniversitySeoulRepublic of Korea

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