Hindawi Publishing Corporation
The Scientific World Journal
Volume 2013, Article ID 528069, 3 pages
http://dx.doi.org/10.1155/2013/528069
Editorial
Swarm Intelligence and Its Applications
Yudong Zhang,1 Praveen Agarwal,2 Vishal Bhatnagar,3 Saeed Balochian,4 and Jie Yan5
1
School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
Department of Mathematics, Anand International College of Engineering, Near Kanota, Agra Road, Jaipur 303012, India
3
Ambedkar Institute of Advanced Communication Technologies and Research, New Delhi, India
4
Department of Electrical Engineering, Islamic Azad University, Gonabad Branch, Gonabad, Iran
5
Suzhou University, Suzhou, Jiangsu, China
2
Correspondence should be addressed to Yudong Zhang; zhangyudongnuaa@gmail.com
Received 16 September 2013; Accepted 16 September 2013
Copyright © 2013 Yudong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems
are typically made up of a population of simple agents interacting locally with one another and with their environment.
The inspiration often comes from nature, especially biological
systems. The agents follow very simple rules, and although
there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree
random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. Natural examples of SI include ant colonies,
bird flocking, animal herding, bacterial growth, and fish
schooling.
Research in SI started in the late 1980s. Besides the applications to conventional optimization problems, SI can be
employed in library materials acquisition, communications,
medical dataset classification, dynamic control, heating system planning, moving objects tracking, and prediction.
Indeed, SI can be applied to a variety of fields in fundamental
research, engineering, industries, and social sciences.
The main objective of this special issue is to provide the
readers with a collection of high quality research articles that
address the broad challenges in application aspects of swarm
intelligence and reflect the emerging trends in state-of-the-art
algorithms.
The special issue received 42 high quality submissions
from different countries all over the world. All submitted
papers followed the same standard (peer-reviewed by at least
three independent reviewers) as applied to regular submissions to “this journal”. Due to the limited space, 15 papers
were finally included. The primary guideline was to demonstrate the wide scope of SI algorithms and applications in
various aspects. Besides, mathematically oriented papers with
promising potential in practical problems were also included.
The paper authored by Y.-L. Wu et al. (National Chiao
Tung University and Ming Chuan University) presents an
integer programming model of the studied problem by
considering how to select materials in order to maximize the
average preference and the budget execution rate under some
practical restrictions including departmental budget and limitation of the number of materials in each category and each
language. They propose a discrete particle swarm optimization (DPSO) with scout particles, design an initialization
algorithm and a penalty function to cope with the constraints,
and employ the scout particles to enhance the exploration
within the solution space.
In the paper by Z. Yin et al. (Harbin Institute of Technology), they propose an efficient multiuser detector based on
a suboptimal code mapping multiuser detector and artificial
bee colony algorithm (SCM-ABC-MUD) and implement the
proposed algorithm in direct-sequence ultrawideband (DSUWB) systems under the additive white Gaussian noise
(AWGN) channel.
M. S. Uzer et al. (Selçuk University) offer a hybrid
approach that uses the artificial bee colony (ABC) algorithm
for feature selection and support vector machines for classification. For the diagnosis of hepatitis, liver disorders, and
diabetes datasets from the UCI database, the proposed system
reached classification accuracies of 94.92%, 74.81%, and
79.29%, respectively.
2
Another paper is by M. Karakose (Fırat University) and
U. Cigdem (Gaziosmanpaşa University). It proposes a new
approach for improvement of DNA computing with adaptive
parameters towards the desired goal using quantum-behaved
particle swarm optimization (QPSO). Experimental results
obtained with MATLAB and FPGA demonstrate ability
to provide effective optimization, considerable convergence
speed, and high accuracy according to DNA computing
algorithm.
In the paper by Y. Celik (Karamanoglu Mehmetbey
University) and E. Ulker (Selcuk University), their research
proposes an improved marriage in honey bees optimization
(IMBO) by adding Levy flight algorithm for queen mating
flight and neighboring for worker drone improving. The
IMBO algorithm’s performance and its success are tested on
the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
M. Baygin (Ardahan University) and M. Karakose (Fırat
University) study a new approach of immune system-based
optimal estimate for dynamic control of group elevator systems. The method is mainly based on estimation of optimal
way by optimizing all calls with genetic, immune system and
DNA computing algorithms, and it is evaluated with a fuzzy
system. With dynamic and adaptive control approach in this
study, a significant progress on group elevator control systems
has been achieved in terms of time and energy efficiency
according to traditional methods.
The paper by M. Karakose (Fırat University) proposes
a reinforcement-learning based artificial immune classifier.
The proposed new approach has many contributions according to other methods in the literature such as effectiveness,
less memory cell, high accuracy, speed, and data adaptability.
Some benchmark data and remote image data are used for
experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative
selection classifier, and resource limited artificial immune
classifier are given to demonstrate the effectiveness of the proposed new method.
In their paper, T. J. Choi et al. (Sungkyunkwan University)
and (Daegu Gyeongbuk Institute of Science and Technology)
present an adaptive parameter control DE algorithm. The
control parameters of each individual are adapted based on
the average of successfully evolved individuals’ parameter values using the Cauchy distribution. The experimental results
show that their proposed algorithm is more robust than the
standard DE algorithm and several state-of-the-art adaptive
DE algorithms in solving various unimodal and multimodal
problems.
In the paper by R.-J. Ma et al. (Southwest Jiaotong University and CSR Qishuyan Institute Co., Ltd.), the authors
present an integral mathematical model and particle swarm
optimization (PSO) algorithm based on the life cycle cost
(LCC) approach for the heating system planning (HSP)
problem. The results show that the improved particle swarm
optimization (IPSO) algorithm can more preferably solve the
HSP problem than PSO algorithm.
In the paper by M. Tang et al. (National University of
Defense Technology and Université Pierre et Marie Curie),
The Scientific World Journal
they report that the flocking has some negative effects on the
human, as the infectious disease H7N9 will easily be transmitted from the denser flocking birds to the human. Their
paper focuses on the H7N9 virus transmission in the flocking
birds and from the flocking birds to the human. Some interesting results have been shown: (1) only some simple rules
could result in an emergence such as the flocking; (2) the minimum distance between birds could affect H7N9 virus transmission in the flocking birds and even affect the virus transmissions from the flocking birds to the human.
Y. Wang et al. (China University of Petroleum) present a
memory-based multiagent coevolution algorithm for robust
tracking the moving objects. Each agent can remember,
retrieve, or forget the appearance of the object through its
own memory system by its own experience. Experimental
results show that their proposed method can deal with large
appearance changes and heavy occlusions when tracking a
moving object.
The paper by Q. Ni and J. Deng (Southeast University and
Soochow University) analyzes the performance of PSO with
the proposed random topologies and explores the relationship between population topology and the performance of
PSO from the perspective of graph theory characteristics in
population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an
extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of
population topology.
Y. Zhou and H. Zheng (Guangxi University for Nationalities, Guangxi Key Laboratory of Hybrid Computation and
IC Design Analysis) propose a novel complex valued cuckoo
search algorithm. They use complex-valued encoding to
expand the information of nest individuals and denote the
gene of individuals by plurality. The value of independent
variables for objective function is determined by modules,
and a sign of them is determined by angles. The position of
nest is divided into real part gene and imaginary gene. Six
typical functions are tested, and the usefulness of the proposed algorithm is verified.
The paper by R. Alwee et al. (Universiti Teknologi
Malaysia) introduces a hybrid model that combines support
vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting.
Particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The experimental results
show that their proposed hybrid model is able to produce
more accurate forecasting results as compared to the individual models.
Finally, K. S. Lim et al. (Universiti Teknologi Malaysia,
Universiti Malaysia Pahang, and University of Malaya)
describe an improved Vector Evaluated Particle Swarm Optimization algorithm by incorporating the nondominated solutions as the guidance for a swarm rather than using the best
solution from another swarm. The results suggest that the
improved Vector Evaluated Particle Swarm Optimization
algorithm has impressive performance compared with the
conventional Vector Evaluated Particle Swarm Optimization
algorithm.
The Scientific World Journal
3
Aknowledgments
We would like to express their gratitude to all of the authors
for their contributions, and the reviewers for their effort providing valuable comments and feedback. We hope this special
issue offers a comprehensive and timely view of the area of
applications of swarm intelligence and that it will offer stimulation for further research.
Yudong Zhang
Praveen Agarwal
Vishal Bhatnagar
Saeed Balochian
Jie Yan
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