Saturday, January 7, 2017

[DMANET] IEEE CEC Special Session & Competition on: Niching Methods for Multimodal Optimization

Call for Papers

2017 IEEE Congress on Evolutionary Computation Special Session and
Competition on: "Niching Methods for Multimodal Optimization"

June 5-8, 2017, at Donostia - San Sebastián, Spain

** Paper Submission: 16 January 2017 **
In the Main research topics drop-down menu please select: *SS36:
Niching Methods for Multimodal Optimization*


Population or single solution search-based optimization algorithms (i.e.
{meta,hyper}-heuristics) in their original forms are usually designed for
locating a single global solution. Representative examples include among others
evolutionary and swarm intelligence algorithms. These search algorithms
typically converge to a single solution because of the global selection scheme
used. Nevertheless, many real-world problems are "multimodal" by nature, i.e.,
multiple satisfactory solutions exist. It may be desirable to locate many such
satisfactory solutions, or even all of them, so that a decision maker can
choose one that is most proper in his/her problem domain. Numerous techniques
have been developed in the past for locating multiple optima (global and/or
local). These techniques are commonly referred to as "niching" methods. A
niching method can be incorporated into a standard search-based optimization
algorithm, in a sequential or parallel way, with an aim to locate multiple
globally optimal or suboptimal solutions. Sequential approaches locate optimal
solutions progressively over time, while parallel approaches promote and
maintain formation of multiple stable subpopulations within a single
population. Many niching methods have been developed in the past, including
crowding, fitness sharing, derating, restricted tournament selection, clearing,
speciation, etc. In more recent times, niching methods have also been developed
for meta-heuristic algorithms such as Particle Swarm Optimization, Differential
Evolution and Evolution Strategies.

Most of existing niching methods, however, have difficulties that need to be
overcome before they can be applied successfully to real-world multimodal
problems. Some identified issues include: difficulties to pre-specify some
niching parameters; difficulties in maintaining found solutions in a run; extra
computational overhead; poor scalability when dimensionality and modality are
high. This special session aims to highlight the latest developments in niching
methods, bringing together researchers from academia and industries, and
exploring future research directions on this topic. We invite authors to submit
original and unpublished work on niching methods. Topics of interest include
but are not limited to:

- Theoretical developments in multimodal optimization
- Niching methods that incurs lower computational costs
- Handling the issue of niching parameters in niching methods
- Handling the scalability issue in niching methods
- Handling problems characterized by massive multi-modality
- Adaptive or parameter-less niching methods
- Multiobjective approaches to niching
- Multimodal optimization in dynamic environments
- Niching methods applied to discrete multimodal optimization problems
- Niching methods applied to constrained multimodal optimization problems
- Niching methods using parallel or distributed computing techniques
- Benchmarking niching methods, including test problem design and performance
- Comparative studies of various niching methods
- Niching methods applied to engineering and other real-world multimodal
optimization problems

Please note that we are NOT interested if the adopted task is to find
a single solution of a multimodal problem.

Furthermore, a companion competition on Niching Methods for Multimodal
Optimization will also be organized in conjunction with our special
session. See further information at:

The aim of the competition is to provide a common platform that encourages fair
and easy comparisons across different niching algorithms. The competition
allows participants to run their own niching algorithms on 20 benchmark
multimodal functions with different characteristics and levels of difficulty.
Researchers are welcome to evaluate their niching algorithms using this
benchmark suite, and report the results by submitting a paper to the associated
niching special session (i.e., submitting via the on-line submission system of
CEC'2017). In case it is too late to submit the paper (i.e., passing the
CEC'2017 submission deadline), author may submit their results in a report
directly to the special session organizers, in order to be counted in the

These events are supported by the newly established IEEE CIS Task Force on
Multi-modal Optimization (

Important Dates

- Paper Submission: 16 January 2017
- Notification of Acceptance: 26 February 2017

Paper Submission:

Manuscripts should be prepared according to the standard format and
page limit of regular papers specified in CEC'2017 and submitted
through the CEC'2017 website: CEC 2017 submissions. Special session
papers will be treated in the same way as regular papers and included
in the conference proceedings.

In the Main research topics drop-down menu please select:
*SS36: Niching Methods for Multimodal Optimization*


Michael G. Epitropakis, Lancaster University, UK.
Xiaodong Li, RMIT University, Australia
Andries Engelbrecht, University of Pretoria, South Africa

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