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Multimodal optimization is often encountered in engineering problems,especially when different and alternative solutions are sought. Evolutionaryalgorithms can efficiently tackle multimodal optimization thanks to theirfeatures such as the concept of population, exploration/exploitation, and beingsuitable for parallel computation. This paper investigates whether a less-knownoptimizer, the Big Bang-Big Crunch (BBBC) algorithm, is suitable for multimodaloptimization. We extended BBBC and propose k-BBBC, a clustering-basedmulti-modal optimizer. Additionally, we introduce two post-processing methodsto (i) identify the local optima in a set of retrieved solutions (i.e., apopulation), and (ii) quantify the number of correctly retrieved optima againstthe expected ones (i.e., success rate). Our results show that k-BBBC performswell even with problems having a large number of optima (tested on $379$optima) and high dimensionality (tested on $32$ decision variables), but itbecomes computationally too expensive for problems with many local optima(i.e., in the CEC'2013 benchmark set). Compared to other multimodaloptimization methods, it outperforms them in terms of accuracy (in both searchand objective space) and success rate (number of correctly retrieved optima)when tested on basic multimodal functions, especially when elitism is applied;however, it requires knowing the number of optima of a problem, which makes itsperformance decrease when tested on niching competition test CEC'2013. Lastly,we validated our proposed post-processing methods by comparing their successrate to the actual one: results suggest that these methods can be used toevaluate the performance of a multimodal optimization algorithm by correctlyidentifying optima and providing an indication of success -- without the needto know where the optima are located in the search space.

Multimodal Optimization with k-Cluster Big Bang-Big Crunch Algorithm and Postprocessing Methods for Identification and Quantification of Optima