SPECIALSESSIONSNICHING METHODS FOR MULTI-MODAL
OPTIMIZATIONMichael G. Epitropakis, Xiaodong Li, Andries Engelbrecht

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 intelligencealgorithms. These search algorithms typically converge to a single solution because of theglobal selection scheme used. Nevertheless, many real-world problems are "multimodal" bynature, 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 ismost proper in his/her problem domain. Numerous techniques have been developed in thepast for locating multiple optima (global and/or local). These techniques are commonlyreferred 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 formationof multiple stable subpopulations within a single population. Many niching methods havebeen developed in the past, including crowding, fitness sharing, derating, restricted
tournament selection, clearing, speciation, etc. In more recent times, niching methods havealso 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 overcomebefore 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 inniching methods, bringing together researchers from academia and industries, and exploring
future research directions on this topic. We invite authors to submit original and unpublishedwork on niching methods.

Topics•

Theoretical developments in multimodal optimization

Special Session Chair
Ke Tang · ketang@ustc.edu.chn

IEEE Conference on Evolutionary Computation
Donostia – San Sebastián, June 8-5
www.cec2017.org · info@cec2017.org

SPECIALSESSIONS•

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

2/2