Dynamic multi-objective evolutionary algorithm with objective space prediction strategy

Guerrero-Peña, Elaine; Araújo, A.F.R.

doi:10.1016/j.asoc.2021.107258

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Thus, MOEAs can take advantage of mechanisms that guide the search and diminish the sampling. Guerrero-Peña and Araújo [134] and Jiang et al [135] proposed DMOEAs using some learning for dealing with DMOPs. These studies proposed using statistical learning or transfer learning to guide the evolutionary search, a way of reducing sampling.…”

Section: Some Promising Research Topicsmentioning

confidence: 99%

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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Section: Some Promising Research Topicsmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

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“…The work [9] presents a hybrid evolution strategy that helps architects generate sets of floor plans at an early design stage. This approach is based on the algorithm of evolution strategies.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The analysis of the works [9][10][11][12][13][14][15][16][17][18] showed that the vast majority of known studies are based on the use of general scientific approaches. Common limitations of these works are:…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a method of structural-parametric assessment of the object state

Mahdi¹,

Zhyvotovskyi

Kravchenko

et al. 2021

EEJET

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A method of structural and parametric assessment of the object state has been developed. The essence of the method is to provide an analysis of the current state of the object under analysis. The key difference of the developed method is the use of advanced procedures for processing undefined initial data, selection, crossover, mutation, formation of the initial population, advanced procedure for training artificial neural networks and rounding coordinates. The use of the method of structural-parametric assessment of the object state allows increasing the efficiency of object state assessment. An objective and complete analysis is achieved using an advanced algorithm of evolution strategies. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. An example of using the proposed method in assessing the operational situation of the troops (forces) grouping is given. The developed method is 30–35 % more efficient in terms of the fitness of the obtained solution compared to the conventional algorithm of evolution strategies. Also, the proposed method is 20–25 % better than the modified algorithms of evolution strategies due to the use of additional improved procedures according to the criterion of fitness of the obtained solution. The proposed method can be used in decision support systems of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, DSS for logistics ACS of the Armed Forces of Ukraine

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