Adaptive Differential Evolution With Evolution Memory for Multiobjective Optimization

Li, Kun; Tian, Huixin

doi:10.1109/access.2018.2885947

Cited by 7 publications

(2 citation statements)

References 39 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the internal behaviour of the optimization process: it is stopped through an internal convergence criterion and not by founding a global minimum [3]. More information and applications on the different optimization algorithms can be seen in [10,21,2,22,26,7,40,35], among others. Therefore, considering that the convergence of the method depends on the Robin parameters as presented in [25], the initial Robin parameter (calculated as the condensed stiffness on the interface estimated from an initial strip of elements and presented in Figure 5) can be modified by means of two factors: α G for the global model and α L for the local fine model, as shown in Eq.…”

Section: Description Of the Optimization Processmentioning

confidence: 99%

Global-Local non intrusive analysis with robin parameters: application to plastic hardening behavior and crack propagation in 2D and 3D structures

et al. 2022

View full text Add to dashboard Cite

The global/local analysis allows to embed a specific local zone of interest with a different behaviour in a global coarse model. In this local model, fine meshes are usually used to model some structural details and potentially non-linear behaviours, such as plastic hardening and crack propagation. The standard global/local approach can be observed as a Dirichlet-Neumann iterative algorithm where a Dirichlet problem on the local model and a Neumann problem on the global one are solved successively. This paper proposes a new approach for the global/local framework as Robin parameters are considered on both local and global models to obtain more flexibility and improvement for convergence. Particularly, Robin parameters are obtained using a pre-defined strip of elements and the results are later improved by means of single-objective optimization, minimizing the number of iterations to achieve convergence. This improvement is illustrated for cracked domains and plastic hardening in 2D problems and 3D elements within a non-intrusive framework, allowing the usage of commercial finite element software along with open-source research finite element software.

show abstract

Section: Description Of the Optimization Processmentioning

confidence: 99%

Global-Local non intrusive analysis with robin parameters: application to plastic hardening behavior and crack propagation in 2D and 3D structures

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In [40], a cooperative DE framework is designed for constrained MOPs, in which multiple DE operators are run in different subpopulations to optimize its own constrained subproblem. In [41], four DE operators are combined and a sliding window is adopted in [42] to provide the reward for each DE operator according to the enhancement on subproblems. Similarly, four DE operator pools are presented in [43] including two DE operators with complementary search patterns in each pool to provide an improved search ability.…”

Section: Indicator-based Moeasmentioning

confidence: 99%

Decomposition-Based Multiobjective Evolutionary Optimization with Adaptive Multiple Gaussian Process Models

Zhang

Gong

et al. 2020

Complexity

View full text Add to dashboard Cite

In recent years, a number of recombination operators have been proposed for multiobjective evolutionary algorithms (MOEAs). One kind of recombination operators is designed based on the Gaussian process model. However, this approach only uses one standard Gaussian process model with fixed variance, which may not work well for solving various multiobjective optimization problems (MOPs). To alleviate this problem, this paper introduces a decomposition-based multiobjective evolutionary optimization with adaptive multiple Gaussian process models, aiming to provide a more effective heuristic search for various MOPs. For selecting a more suitable Gaussian process model, an adaptive selection strategy is designed by using the performance enhancements on a number of decomposed subproblems. In this way, our proposed algorithm owns more search patterns and is able to produce more diversified solutions. The performance of our algorithm is validated when solving some well-known F, UF, and WFG test instances, and the experiments confirm that our algorithm shows some superiorities over six competitive MOEAs.

show abstract

Memory-Driven Metaheuristics: Improving Optimization Performance

Farahmand-Tabar

2023

Handbook of Formal Optimization

View full text Add to dashboard Cite

Metaheuristics are stochastic optimization algorithms that mimic natural processes to find optimal solutions to complex problems. The success of metaheuristics largely depends on the ability to effectively explore and exploit the search space. Memory mechanisms have been introduced in several popular metaheuristic algorithms to enhance their performance. This chapter explores the significance of memory in metaheuristic algorithms and provides insights from well-known algorithms. The chapter begins by introducing the concept of memory, and its role in metaheuristic algorithms. The key factors influencing the effectiveness of memory mechanisms are discussed, such as the size of the memory, the information stored in memory, and the rate of information decay. A comprehensive analysis of how memory mechanisms are incorporated into popular metaheuristic algorithms is presented, and concludes by highlighting the importance of memory in metaheuristic performance and providing future research directions for improving memory mechanisms. The key takeaways are that memory mechanisms can significantly enhance the performance of metaheuristics by enabling them to explore and exploit the search space effectively and efficiently, and that the choice of memory mechanism should be tailored to the problem domain and the characteristics of the search space.

show abstract

Adaptive Differential Evolution With Evolution Memory for Multiobjective Optimization

Cited by 7 publications

References 39 publications

Global-Local non intrusive analysis with robin parameters: application to plastic hardening behavior and crack propagation in 2D and 3D structures

Global-Local non intrusive analysis with robin parameters: application to plastic hardening behavior and crack propagation in 2D and 3D structures

Decomposition-Based Multiobjective Evolutionary Optimization with Adaptive Multiple Gaussian Process Models

Memory-Driven Metaheuristics: Improving Optimization Performance

Contact Info

Product

Resources

About