Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art

By Swagatam Das, Sayan Maity, B. Qu, Ponnuthurai Nagaratnam Suganthan. Swarm and Evolutionary Computation, 2011

318 citations recorded by the source corpus.

Abstract Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable one while still maintaining the optimal system performance. Evolutionary Algorithms (EAs), due to their population-based approaches, are able to detect multiple solutions within a population in a single simulation run and have a clear advantage over the classical optimization techniques, which need multiple restarts and multiple runs in the hope that a different solution may be discovered every run, with no guarantee however. Numerous evolutionary optimization techniques have been developed since late 1970s for locating multiple optima (global or local). These techniques are commonly referred to as “niching” methods. Niching can be incorporated into a standard EA to promote and maintain formation of multiple stable subpopulations within a single population, with an aim to locate multiple globally optimal or suboptimal solutions simultaneously. This article is the first of its kind to present a comprehensive review of the basic concepts related to real-parameter evolutionary multimodal optimization, a survey of the major niching techniques, a detailed account of the adaptation of EAs from diverse paradigms to tackle multimodal problems, benchmark problems and performance measures.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7f269c4d0877ada881e7877f317e31486997547c

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Citations in the corpus

Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, background): “Das et al. (2011) presented a comprehensive review of the basic concepts related to real-parameter evolutionary multimodal optimization, a survey of the major evolutionary optimization techniques, a detailed account of the adaptation of evolutionary algorithms from diverse paradigms to tackle…”
Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems (2018, background): “As the classic Newtonian and gradient-based techniques are not suitable to perform global optimization, a exponential growth of researches on new stochastic metaheuristics for handling global optimization is noted in recent literature [1]–[3].”
A comprehensive review of firefly algorithms (2013, background): “In his paper [33], Yang established that the new FA was especially suitable for multi-modal optimization applications [34].”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “This aspect can be particularly observed in multimodal optimization [78, 79].”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “For an extensive survey on multimodal optimization see [40].”
Bio-inspired computation: Where we stand and what's next (2019, background): “Multimodal optimization problems consist of finding multiple optimal solutions within a single algorithm run, in order to have a better knowledge about the different solutions in the search space [156].”