[alife] CFP - ACM TAAS Special Issue - Foraging Theory Based Optimization Algorithms

[Ajith Abraham]

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Call for Papers - Special Issue on Foraging Theory Based Optimization
Algorithms
http://www.softcomputing.net/acm2009.html
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ACM TRANSACTIONS on AUTONOMOUS and ADAPTIVE SYSTEMS
http://taas.acm.org/



I. Aim and Scope
Foraging means the act of searching for food and it forms an integral
part of the daily life of most of the living creatures. Natural
organisms forage in such a way as to maximize their energy intake per
unit time. In other words, they strive to find, capture, and consume
food containing the most calories while expending the least amount of
time possible in doing so. Thus, considering all the constraints
presented by their own physiology such as sensing and cognitive
capabilities, environment (e.g. density of prey, risks from predators,
and physical characteristics of the search space) etc., the natural
foraging strategy that these organisms follow, can lead to
optimization. Inspired by the conceptions emerging from foraging
theory and sometimes based on their wide generalizations, in recent
past, a handful of algorithms have been developed for solving nearly
intractable numerical and combinatorial optimization problems in
engineering. Most prominent among them are the Ant Systems (AS) and
Ant Colony Optimization (ACO), Bacterial Foraging Optimization (BFO),
and the algorithms based on bees social foraging. They have recently
been shown to produce superior results in a wide variety of real-world
applications. Some of the algorithms even use the concept of foraging
in much more general sense, e.g. human beings forage for information,
musician forage for a perfect state of harmony of the notes being
played (E.g. the Harmony Search Algorithm).

During the last five years, research on and with the foraging-based
optimization techniques like ACO, BFOA, Bees Foraging, Artificial Bee
Colony etc. has reached a very promising state. But there is still a
long way to go in order to fully utilize the potential of the
artificial foraging algorithms. This special issue aims at bringing
researchers from academia and industry together to report and review
the latest progresses in this rapidly emerged field, to explore future
directions of research, and to publicize the foraging based algorithms
and metaheuristics to a wider audience

II.     Topics Covered

Authors are invited to submit their original and unpublished work in
the areas including (but not limited to) the following:

1) Theoretical and empirical study of foraging based algorithms like:
The Ant Colony Optimization (ACO), The Bacterial Foraging Optimization
Algorithm (BFOA),    Honey Bee Social Foraging Algorithms, Simulated
    Waggle Dance, Adaptive Bird Flocking Algorithm, Harmony Search
(HS) with special emphasis on mathematical modeling and dynamical
analysis for investigating issues like convergence, stability, and
robustness.

2) Connections to / comparison with other powerful swarm and
evolutionary computing algorithms like Particle Swarm Optimization,
Genetic Algorithms, Differential Evolution etc.

3) Development, benchmarking, and evaluation of new foraging based algorithms.

4) Parameter automation and self-adaptation in foraging based
optimization techniques

5) Foraging theory based algorithms for optimization in dynamic and
noisy environments

6) Foraging theory based algorithms for constrained, niching and
multi-objective optimization

7) Applications to diverse domains including: Swarm robotics, Adaptive
and optimal control, Optimal dynamics  resource allocation problems,
Distributed control of uninhabited autonomous vehicles, Fuzzy/neural
controller design for nonlinear systems, Training artificial neural
networks for pattern recognition, Clustering and Classification,
Forming manufacturing cells, Optimal scheduling of jobs for a
production machine, Telecommunication network routing and network
optimization, Financial prediction, Econometrics, and Business
Intelligence, Generalized assignment problems, Power Systems, Robust
multi-agent system design

Authors should prepare manuscripts according to the ACM accepted
manuscript preparation guidelines. Please visit the following link:

http://www.acm.org/pubs/submissions/submission.htm

Since each ACM TAAS issue must be no longer than 100 pages, as a
guidance papers should not exceed 20 pages.


III.     Important Deadlines

·   December 31, 2009, Submission deadline
·   May 1, 2010, Notification of the first-round review
·   August 1, 2010, Revised submission due
·   October 30, 2010, Final notice of acceptance/reject
·   November 30, 2010, Final manuscript

The expected publication time of the special issue will be in 2011.

IV.     Guest Editors

Athanasios Vasilakos
University of Western Macedonia, Greece,
E-mail: vasilako at csi.forth.gr

Ajith Abraham
Machine Intelligence Research Labs (MIR Labs)
http://www.mirlabs.org
http://www.softcomputing.net
E-mail:  ajith.abraham at ieee.org

Swagatam Das
Department of Electronics and Telecommunications Engineering,
Jadavpur University, Calcutta – 700 032, India
E-mail: swagatamdas19 at yahoo.co.in