%0 Conference Paper %1 6557558 %A Bandaru, S. %A Deb, K. %B Evolutionary Computation (CEC), 2013 IEEE Congress on %D 2013 %K bi-objective multimodal niching optimization parameterless pso %P 95-102 %R 10.1109/CEC.2013.6557558 %T A parameterless-niching-assisted bi-objective approach to multimodal optimization %U http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557558&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6552460%2F6557545%2F06557558.pdf%3Farnumber%3D6557558 %X Evolutionary algorithms are becoming increasingly popular for multimodal and multi-objective optimization. Their population based nature allows them to be modified in a way so as to locate and preserve multiple optimal solutions (referred to as Pareto-optimal solutions in multi-objective optimization). These modifications are called niching methods, particularly in the context of multimodal optimization. In evolutionary multiobjective optimization, the concept of dominance and diversity preservation inherently causes niching. This paper proposes an approach to multimodal optimization which combines this power of dominance with traditional variable-space niching. The approach is implemented within the NSGA-II framework and its performance is studied on 20 benchmark problems. The simplicity of the approach and the absence of any special niching parameters are the hallmarks of this study.

@inproceedings{6557558, abstract = {Evolutionary algorithms are becoming increasingly popular for multimodal and multi-objective optimization. Their population based nature allows them to be modified in a way so as to locate and preserve multiple optimal solutions (referred to as Pareto-optimal solutions in multi-objective optimization). These modifications are called niching methods, particularly in the context of multimodal optimization. In evolutionary multiobjective optimization, the concept of dominance and diversity preservation inherently causes niching. This paper proposes an approach to multimodal optimization which combines this power of dominance with traditional variable-space niching. The approach is implemented within the NSGA-II framework and its performance is studied on 20 benchmark problems. The simplicity of the approach and the absence of any special niching parameters are the hallmarks of this study.}, added-at = {2014-08-05T05:27:58.000+0200}, author = {Bandaru, S. and Deb, K.}, biburl = {https://www.bibsonomy.org/bibtex/2ba791c0c46d4850a2a7ac347894554f2/marcioweck}, booktitle = {Evolutionary Computation (CEC), 2013 IEEE Congress on}, description = {IEEE Xplore Abstract - A parameterless-niching-assisted bi-objective approach to multimodal optimization}, doi = {10.1109/CEC.2013.6557558}, interhash = {a34b97285ae1f61e951a87f5ae3701ff}, intrahash = {ba791c0c46d4850a2a7ac347894554f2}, keywords = {bi-objective multimodal niching optimization parameterless pso}, month = {June}, pages = {95-102}, timestamp = {2014-08-05T05:27:58.000+0200}, title = {A parameterless-niching-assisted bi-objective approach to multimodal optimization}, url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6557558&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6552460%2F6557545%2F06557558.pdf%3Farnumber%3D6557558}, year = 2013 }