ISSN (Print): 2666-2558
ISSN (Online): 2666-2566
Volume 13, 6 Issues, 2020
ISSN (Print): 2666-2558
ISSN (Online): 2666-2566
Aims & Scope
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Editor-in-Chief:Francesco BenedettoSP4TE - Signal Processing for Telecommunications and Economics LaboratoryUniversity of RomaRomeItaly
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Over the past few decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other
complex problems in the real world. Swarm Intelligence models are inspired by social behaviours of simple agents interacting with
each other as well as with the environment, e.g., foraging behavior of ants and bees, flocking of birds, schooling of fish, etc. [1-3].
The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals.
Algorithms, applications and methodologies of the Swarm Intelligence approach explore the emerging realm of swarm intelligence
that finds its basis in the natural behaviour of animals.
The special issue of the Journal titled “Recent Advances in Computer Science and Communications” is an excellent collection
of review and research articles in the field of swarm intelligence-related algorithms, methodologies and applications. An
open call for paper was issued for this special issue. The guest editors feel happy to announce this special issue of the most reputed
journal of Bentham Science.
From a wide range of interesting research papers on various aspects of swarm intelligence, the guest editors, after undergoing
exhaustive peer-reviews from experienced and well-known reviewers, have carefully selected 6 research papers and 1 review.
The final decision for the inclusion of 6 research and 1 review papers has been strictly based on the outcome of the rigorous
peer-review process, shortlisting successful research papers by researchers as per reviewers’ comments and guidelines.
A brief summary of the research papers included in this special issue is enlisted as follows:
The first article by Ranjendra Singh, Anurag Singh and Arun Solanki  titled “A Binary Particle Swarm Optimization for IC
floorplanning” proposed a novel SI based algorithm “Binary Particle Swarm Optimization” combined with floor plan representation
to optimize the area and wire length for a fixed outline floorplan. The experimental results on the Microelectronic Center of
North Carolina (MCNC) validated the proposed BPSO algorithm towards better convergence for area and wire length optimization,
as compared to other meta-heuristic algorithms. The results obtained were compared with the solutions derived from other
meta-heuristic algorithms, and it was found that area is improvised up to 10% and the wire length is improvised up to 28%.
The second article by Amandeep Kaur Virk and Kawaljeet Singh  titled “On Performance of Binary Flower Pollination
Algorithm for Rectangular Packing Problem” assessed the performance of recent metaheuristic approach named “Binary Flower
Pollination Algorithm” for rectangle packing optimization problem, which was employed to search the optimal placement
order and optimal layout. The algorithm was tested on benchmark datasets and the simulation results proved that the performance
of binary flower pollination algorithms is the best as compared to other existing metaheuristic approaches.
The third article by Sandeep Kumar, Anand Nayyar, Nhu Gia Nguyen and Rajani Kumari titled  “Hyperbolic Spider
Monkey Optimization Algorithm” studied various perturbation techniques used in spider monkey optimization algorithms and
proposed a novel algorithm titled “Hyperbolic Spider Monkey Optimization Algorithm” inspired by hyperbolic growth function.
The proposed algorithm was tested over a set of 23 CEC 2005 benchmark problems and it was observed that the proposed
algorithm is better as compared to other approaches in terms of improved perturbation rate, desirable convergence precision,
rapid convergence rate and improved global search capability.
The fourth article by Avinash Kaur, Pooja Gupta and Manpreet Singh titled  “A Data Placement Strategy Based on Crow
Search Algorithm in Cloud Computing” proposed a novel data placement strategy based on Crow Search Algorithm (CSA) to
dynamically distribute the data sets to appropriate data center’s during the runtime stage of the workflow. Simulation-based
results proved that the CSA outperforms in locating the best data center for data placement for best workflow management as
compared to other algorithms.
The fifth article by Asima Kukkar and Rajni Mohana titled  “Bug Report Summarization by using Swarm Intelligence
Approaches” proposed a novel approach for the extraction of crucial information from extensive reports to summarize the problem
in short description. The objective of this paper was to generate an unsupervised extractive bug report summarization system
to apply on any dataset without much effort and high cost for creating manual summaries of dataset, to handle comments
and summaries in an effective manner, reduce data sparsity, information and redundancy for lengthy data set and to provide
accurate summary information. The proposed approach was tested with other supervised and unsupervised approaches and it
was concluded that the Hybrid swarm intelligence approach provides better results.
The sixth article by Soniya Lalwani, Harish Sharma and Kusum Deep titled  “An Implementation of Three-Level Multi-
Objective ABC Algorithm for RNA Multiple Structural Alignment” presented Artificial Bee Colony algorithm based threelevel
multi-objective approach for performing structural alignment of RNA sequences i.e. MO-3LABC. MO-3LABC algorithm
was compared with MO-TLPSO algorithm and results were compared for pairwise and multiple sequence alignment datasets for prediction accuracy and solution quality criteria. It was proved that MO-3LABC outperforms MO-TLPSO in all evaluation
The seventh article by Chinwe Igiri, Yudhveer Singh and Ramesh F.C. Poonia titled  “A Review Study of Modified
Swarm Intelligence: Particle Swarm Optimization, Firefly, Bat and Gray Wolf Optimizer Algorithms” explored the improvement
strategies of various swarm intelligence algorithms with regard to PSO, Firefly, Bat and Gray Wolf optimizer with a primary
objective to understand the trends and relationships among their performance.
The main aim of this special issue is to enlighten the researchers regarding the latest methodologies, algorithms and applications
with regard to Swarm Intelligence. It is expected that these papers can benefit students, researchers and academicians to
do advanced work in the area of swarm intelligence. With this special issue, there is a strong convincing evidence that Swarm
Intelligence plays a crucial role in optimizing tremendous problems in diverse areas of computer science.
We would like to thank the Editor-in-Chief of the journal, Professor Francesco Benedetto for his huge support for this issue.
Our special thanks go to all editorial staff, especially Wajeeha Syed, Wajeeha Ahmed and Raheela Anjum for their valuable
and prompt support throughout the preparation and publication of this special issue. We express our deep thanks to all authors
for their novel contributions to this special issue. We also extend our thanks to all the reviewers for their time, devotion, hard
work and on-time precision response to ensure the high-quality review of the accepted papers.