Abstract
Multimodal optimization is used to find multiple global & local optima which is very useful in many real world optimization problems. But often evolutionary algorithms fail to locate multiple optima as required by the system. Also they fail to store those optima by themselves. So we have to use other selection scheme that can detect & store multiple optima along with evolutionary algorithms. Hence we use niching which is a very powerful tool in detecting & storing multiple optima. Niching methods were introduced to EAs to allow maintenance of a population of diverse individuals so that multiple optima within a single population can be located .Crowding which is a very primitive branch of niching is used here as the selection scheme with Invasive Weed Optimization (IWO) which is a ecologically inspired algorithms depicting behaviors of plants . For multimodal optimization the total search space is divided into several niches in which separately IWO is applied to find the optima in niches. The niches will also store this optima within themselves.
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Majumdar, R. et al. (2012). Artificial Weed Colonies with Neighbourhood Crowding Scheme for Multimodal Optimization. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 130. Springer, India. https://doi.org/10.1007/978-81-322-0487-9_74
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