Various challenges in real life are multi-objective and conflicting (i.e., alter concurrent optimization). This implies that a single objective is optimized based on another’s cost. The Multi-Objective Optimization (MOO) issues are challenging but potentially realistic, and due to their wide-range application, optimization challenges have widely been analyzed by research with distinct scholarly bases. Resultantly, this has yielded distinct approaches for mitigating these challenges. There is a wide-range literature concerning the approaches used to handle MOO challenges. It is important to keep in mind that each technique has its pros and limitations, and there is no optimum alternative for cure searchers in a typical scenario. The MOO challenges can be identified in various segments e.g., path optimization, airplane design, automobile design and finance, among others. This contribution presents a survey of prevailing MOO challenges and swarm intelligence approaches to mitigate these challenges. The main purpose of this contribution is to present a basis of understanding on MOO challenges.
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Rich Caruana
Rich Caruana
School of Computing, The University of Utah, UT 84112, United States.
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Rich Caruana and Yin Lou, “A Survey on Multi Objective Optimization Challenges in Swarm Intelligence”, Journal of Computing and Natural Science, vol.1, no.4, pp. 121-129, October 2021. doi: 10.53759/181X/JCNS202101018.
