Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms

Sahmoud, Shaaban; Topcuoglu, Haluk Rahmi

doi:10.1016/j.asoc.2019.105783

Cited by 23 publications

(8 citation statements)

References 21 publications

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“…If the estimated severity level is lower than θ min , some population members will be replaced by mutated candidates of existing solutions. This severity-based approach is integrated into the framework of NSGA-II, showing encouraging results as reported in [134].…”

Section: Dynamics-based Approachesmentioning

confidence: 70%

“…Sahmoud and Topcuoglu [134] argued that Liu et al's approach [98] has some drawbacks: 1) the severity estimation method using average deviation values may underestimate the severity of change particularly when one objective function encounters severe changes while the others are not afected; 2) the estimated severity for the new environment requires historical severity information, which therefore may not accurately relect the actual severity of change for the current environment. Accordingly, they proposed a new approach to measure the severity of change.…”

Section: Dynamics-based Approachesmentioning

confidence: 99%

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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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Section: Dynamics-based Approachesmentioning

confidence: 70%

Section: Dynamics-based Approachesmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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“…The popular change detection manner contains the population-and sensor-based methods [1], [44], and in this study, we hold the belief that it is important to not only detect but also quantify the environmental changes, which is conducive to adopting appropriate response strategies. In this regard, half of the non-dominated solutions in the previous environment are selected to form the sensor set S, which is used to estimate the change degree (denoted by CD) of objective function as [46]:…”

Section: A Environmental Change Quantificationmentioning

confidence: 99%

“…A noticeable issue is that the most existing methods have paid a great many of attention to designing effective response strategies, while the in-depth analysis is relatively limited on the dynamic behaviors (e.g., the changing severity), whose precise characterization is conducive to tracking the varying Pareto front [18], [46]. Even in those emerging hybrid algorithms that have applied different novel response methods, few attention has been paid to the quantification of the environmental changes, which motivates us to cover this research gap.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

Wang

Lan

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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In this paper, a novel dynamic multi-objective optimization algorithm (DMOA) is proposed based on a designed hierarchical response system (HRS). Named as HRS-DMOA, the proposed algorithm mainly aims at integrating merits from the mainstream ideas of dynamic behavior handling (i.e., the diversity-, memory-, and prediction-based methods) so as to make flexible responses to environmental changes. In particular, by two pre-defined thresholds, the environmental changes are quantified as three levels. In case of a slight environmental change, the previous Pareto set-based refinement strategy is recommended, while the diversity-based re-initialization method is applied in case of a dramatic environmental change. For changes occurring in a medium level, the transfer-learning-based response is adopted to make full use of the historical searching experiences. The proposed HRS-DMOA is comprehensively evaluated on a series of benchmark functions, and the results show an improved comprehensive performance as compared with four popular baseline DMOAs in terms of both convergence and diversity, which also outperforms other two state-of-the-art DMOAs in 10 out of 14 testing cases, exhibiting the competitiveness and superiority of the algorithm. Finally, extensive ablation studies are carried out, and from the results, it is found that as compared with randomly selecting the response methods, the proposed HRS enables more reasonable and efficient responses in most cases. In addition, the generalization ability of the proposed HRS as a flexible plug-and-play module to handle dynamic behaviors is proven as well.

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“…Sahmoud and Topcuoglu [136] and Zou et al [137] call attention to the crucial role played by the change detection mechanism in DMOEAs. A particular CDM often works for a problem instance or a group of them.…”

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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Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms

Cited by 23 publications

References 21 publications

Evolutionary Dynamic Multi-objective Optimisation: A Survey

Evolutionary Dynamic Multi-objective Optimisation: A Survey

A Novel Dynamic Multiobjective Optimization Algorithm With Hierarchical Response System

Dynamic Multi-Objective Evolutionary Algorithms: An Overview

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