A New Dynamic Multi-objective Evolutionary Algorithm without Change Detector

Guerrero-Peña, Elaine; Araújo, A.F.R.

doi:10.1109/smc.2019.8913923

Cited by 1 publication

(2 citation statements)

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“…In recent years, several DMOEAs have used combinations of previously defined variation operators. For instance, DE operators have been used in combination with polynomial mutation operators [13,[45][46][47] to improve algorithm performance. Wang and Li [26] developed an adaptive genetic and differential mutation operator.…”

Section: Variation Operatorsmentioning

confidence: 99%

“…Before applying the variation operators, MRP-MOEA performs a local search instead of searching across the entire search space. DNSDE+LS/HV (dynamic NSDE improvement with Gaussian mixture model-based local search) [46] inserts diversity using the Gaussian mixture modelbased local search (GMM-LS) strategy. GMM-LS is triggered in specific generations of the evolutionary process, regardless of the occurrences of changes.…”

Section: Dynamics Approaches Based On Moeasmentioning

confidence: 99%

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Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

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Evolutionary algorithms have been widely explored and applied in optimization problems. The introduction of multi-objective evolutionary algorithms (MOEAs) has facilitated the adaptation and creation of new methods to handle more complex and realistic optimizations, such as dynamic multi-objective optimization problems (DMOPs). A dynamic MOEA (DMOEA) can be constructed by changing the MOEA structure and variation operators used to solve DMOPs. Furthermore, DMOEAs can implement change-detection strategies and mechanisms to handle the dynamics of the environment. DMOEAs are often designed to solve unconstrained DMOPs. However, several studies have been conducted to solve dynamic constrained MOPs and more recently, to solve dynamic many-objective optimization problems. There are many real-world DMOPs; however, few DMOEAs have been evaluated with respect to those problems. Evaluation of algorithm performance is an essential aspect of using DMOEAs. Several measures used to validate MOEAs have been adapted to assess DMOEAs and quantitatively compare them. This paper presents a broad review of DMOEAs. The information is summarized and organized according to current research branches. Furthermore, a more general classification scheme for DMOEAs is proposed, based on the method of incorporating diversity to solve DMOPs.

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