A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Ou, Junwei; Xing, Lining; Liu, Min; Yang, Lihua

doi:10.1109/access.2019.2961980

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…Typical operators include the differential mutation [42] (with mutation step scale F ) and binary recombination (with crossover rate CR). The polynomial mutation [43] (with the index parameter η m ) and simulated binary crossover (SBX, with the distribution index η c ) operators are further options [44].…”

Section: Variation Operatorsmentioning

confidence: 99%

“…RV measures the significance of the change part of the POS that cannot be captured by pure translation and adjusts the strength of that variation proportionally. Ou et al [44] proposed a prediction strategy based on the change degree of decision variables (CDDV). It aims to detect the influence that environmental change causes upon each decision variable, dividing them into two categories: small and significant changes for each dimension.…”

Section: Prediction-based Approachesmentioning

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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Section: Variation Operatorsmentioning

confidence: 99%

Section: Prediction-based Approachesmentioning

confidence: 99%

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Guerrero-Peña,

Araújo,

Garrozi

2023

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

show abstract

“…Ou et al [119] argued that the efect of changes on diferent decision variables may be diferent and therefore response measures should be diferent as well. They calculated for each decision variable the severity of change, according to which variable reinitialisation is made in response to changes.…”

Section: Dynamics-based Approachesmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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“…Commonly used carbon emission accounting methods include actual measurement method [6], material balance algorithm and coefficient method [7]. The actual measurement method is to calculate the total carbon emissions by measuring the flow rate [8], velocity and concentration of the exhaust gas through the instruments and facilities recognized by the relevant departments.…”

Section: Total Industrial Carbon Emissions and Carbon Emissions Intensitymentioning

confidence: 99%

“…The algorithm simplification is completed, and the experiment is ready to begin. (6) The standard coal conversion coefficient here is calculated based on the average low calorific value of the energy. When the conversion coefficient of certain energy is when the interval is selected, the middle value of the interval is used as the conversion coefficient of the energy.…”

Section: Overview Of Interval Multi-objective Optimization Problemsmentioning

confidence: 99%

Carbon emission factor decomposition and carbon peak prediction based on multi-objective decision and information fusion processing

Shi

Feng

2021

EURASIP J. Adv. Signal Process.

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Global warming caused by excessive carbon dioxide emissions has seriously threatened the sustainable development of human society. How to reduce carbon dioxide emissions has become a common problem faced by the international community. This article aims to study the decomposition of carbon emission factors and the prediction of carbon peaks from the perspective of multi-objective decision-making and information fusion processing. The sample collection method and statistical analysis method are used to collect samples and simplify the algorithm. A collection experiment of carbon emission factors based on the industry of City A is designed. The experimental data collection takes into account the conversion of coal and oil products into standard coal and carbon dioxide the resulting emissions impact. The experimental results in this paper show that the simulated and real values of my country’s petroleum carbon emissions have both increased from 2000 to 2015, and the decline will be controlled in 2017. Both the simulated value and the real value of my country's coal carbon emissions have been on the rise from 2000 to 2015, and the decline will be controlled in 2017. The carbon emissions of coal are far greater than those of petroleum. The research on carbon emission factor decomposition and carbon peak prediction based on multi-objective decision-making and information fusion processing has been completed well. The research results can be used for industrial carbon emission factor decomposition and carbon peak prediction in other cities across the country.

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A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Cited by 8 publications

References 42 publications

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Evolutionary Dynamic Multi-objective Optimisation: A Survey

Carbon emission factor decomposition and carbon peak prediction based on multi-objective decision and information fusion processing

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