Maximizing Diversity for Multimodal Optimization
This is an incremental improvement for researchers in optimization algorithms, focusing on enhancing diversity in multimodal optimization.
The paper tackles the problem of locating multiple optima in multimodal optimization by proposing to use the Line Distance measure as the main objective-function, aiming to find multiple solutions simultaneously in a population.
Most multimodal optimization algorithms use the so called \textit{niching methods}~\cite{mahfoud1995niching} in order to promote diversity during optimization, while others, like \textit{Artificial Immune Systems}~\cite{de2010conceptual} try to find multiple solutions as its main objective. One of such algorithms, called \textit{dopt-aiNet}~\cite{de2005artificial}, introduced the Line Distance that measures the distance between two solutions regarding their basis of attraction. In this short abstract I propose the use of the Line Distance measure as the main objective-function in order to locate multiple optima at once in a population.