A new learning based dynamic multi-objective optimisation evolutionary algorithm

Fu, Xiaogang; Sun, Jianyong

doi:10.1109/cec.2017.7969332

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…The method proposed in [61] consists of three steps: 1) prediction based on the direction of movement of the center points to reallocate a series of solutions close to the new POF; 2) a gradual search to produce some well-distributed solutions; 3) the production of random individuals close to the next likely POS. A new learning strategy predicts the new positions of POS in [83] based on the incorporation of mutual information, a stable matching strategy, and Newton's laws of motion. Prediction strategy based on center points and knee points (CKPS) [62] consists of three mechanisms: 1) the forward-looking center points method for predicting the non-dominated set; 2) the knee point method to accurately predict the location and distribution of the new POF; 3) the adaptive diversity maintenance method to generate new random individuals.…”

Section: Prediction-based Approachesmentioning

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: 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

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“…Enhancements in the multi-objective space include studies that predict characteristic points [14,34,35,46] who presents a multi-directional prediction strategy to enhance the performance of evolutionary algorithms in solving a dynamic multi-objective optimization problem. The population is clustered into a number of groups by a proposed classification strategy and used predict the moving location of the Pareto set.…”

Section: Evolutionary Dynamic Optimization (Edo)mentioning

confidence: 99%

A new moving peaks benchmark with attractors for dynamic evolutionary algorithms

Fox

Yang

Caraffini

2022

Swarm and Evolutionary Computation

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“…The study of multi-objective evolutionary algorithms entered a period of full development in the late 1980s, and the method has been successfully applied in several fields. The optimization algorithm establishes an objective function based on your desired goal while translating the constraints to be considered into various mathematical constraints and finding the optimal solution in a given range by computer operations [1][2][3][4]. The great superiority of this approach in dealing with complexity, constraints, and nonlinearity has attracted more and more researchers to initiate research on the use of optimization algorithms in the architectural design phase [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Evolutionary Algorithm Supported Construction of Architectural Window Design Model Based on Visual Comfort - A Case Study of Office Buildings in Cold Regions

Liu,

Yang,

Leng

2023

Applied Mathematics and Nonlinear Sciences

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With the accelerated advancement of big data Internet development, people gradually realize the urgency and importance of energy saving, so the multi-objective optimization research for the performance of office buildings in cold regions is significant, and this paper focuses on the construction of a building window design model based on visual comfort supported by multi-objective evolutionary algorithm- taking office buildings in cold regions as an example, firstly, by reviewing the literature to understand the principle, algorithm, and concept of multi-objective evolution, construct the building window design model based on visual comfort according to the objective function and extract the standard model of inner corridor slab space building in office buildings in cold regions of Harbin and Shenyang. The optimized design of building window parameters was carried out using the established joint simulation and optimization work platform. The results show that for Harbin, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. An increase in window heat transfer coefficient by 0.1W/ (m 2 ∙K) Increases are cooling energy consumption by 0.18%, heating energy consumption by 0.78%, and total energy consumption by 0.46%. For Shenyang, as the window heat transfer coefficient increases, the heating energy consumption increases, the cooling energy consumption increases more slowly, and the total energy consumption increases linearly. If the heat transfer coefficient of the window increases by 0.1W/ (m 2 ∙K), cooling energy consumption increases by 0.18%, heating energy consumption increases by 0.78%, and total energy consumption increases by 0.46%. This study provides a theoretical basis for extracting standard models for other building types while making the results more generalizable and improving the efficiency of sustainable office building design.

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A new learning based dynamic multi-objective optimisation evolutionary algorithm

Cited by 4 publications

References 23 publications

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

A new moving peaks benchmark with attractors for dynamic evolutionary algorithms

Multi-objective Evolutionary Algorithm Supported Construction of Architectural Window Design Model Based on Visual Comfort - A Case Study of Office Buildings in Cold Regions

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