Niching Methods for Multimodal Optimization


Populatino-based meta-heuristic algorithms such as Evolutionary Algorithms (EAs) in their original forms are usually designed for locating a single global solution. These 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 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 or local). These techniques are commonly referred to as “niching” methods. A niching method can be incorporated into a standard EA to promote and maintain formation of multiple stable subpopulations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions. Many niching methods have been developed in the past, including crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation, etc.

Most of existing niching methods, however, have difficulties which 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 is high. This special session aims to highlight the latest developments in niching methods, bring together researchers from academia and industries, and explore 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 We invite authors to submit original and unpublished work on niching methods.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 metrics
  • 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 previously proposed benchmark suite may help the interested researchers to evaluate and compare their niching algorithms with the state-of-the-art methodologies in the field. Further information can be found in IEEE CEC'2013 Competition on Niching Methods for Multimodal Optimization . Briefly, the aim of this benchmark suite is to provide a common platform that encourages fair and easy comparisons across different niching algorithms. The benchmark suite allows researchers to run their own niching algorithms on 20 benchmark multimodal functions with different characteristics and levels of difficulty.

Paper Submission:

Manuscripts should be prepared according to the standard format and page limit of regular papers specified in WCCI'2014 and submitted through the WCCI'2014 website: Special session papers will be treated in the same way as regular papers and included in the conference proceedings.

Important Dates:

  • Paper Submission: 20 January 2014 (extended final deadline)
  • Notification of Acceptance:  15 March 2014
  • Final Paper Submission: 15 April 2014

Technical Committee:

  • Patrick  Siarry, Universite Paris-Est Creteil Val-de-Marne, France
  • Jing Liang, Zhengzhou University, China
  • Matthys du Plessis, Nelson Mandela Metropolitan University, South Africa
  • Konstantinos E. Parsopoulos, University of Ioannina, Greece
  • Jonathan Mwaura, University of Pretoria, South Africa
  • Mike Preuss, Dortmund University, Germany
  • Nicos Pavlidis, Lancaster University, UK
  • Vassilis P. Plagianakos, University of Central Greece, Greece
  • Jian-Ping Li, Bradford University, UK
  • Bruno Sareni, Universite de Toulouse, France
  • Ofer Shir, Haifa Research Lab, Israel
  • Grant Dick, University of Otago, NZ
  • Michael N. Vrahatis, University of Patras, Greece

Special Session Organizers:

Associate Professor Xiaodong Li

School of Computer Science and Information Technology

RMIT University

Melbourne, VIC 3001, Australia



Professor Andries Engelbrecht

South African Research Chair in Artificial Intelligence

Department of Computer Science, School of Information Technology

University of Pretoria

Pretoria 0002, South Africa



Dr. Michael G. Epitropakis

Computing Science and Mathematics, School of Natural Sciences

University of Stirling,

Stirling FK9 4LA, Scotland