A diversity introduction strategy based on change intensity for evolutionary dynamic multiobjective optimization

Liu, Ruochen; Peng, Luyao; Liu, Jiangdi; Liu, Jing

doi:10.1007/s00500-020-05175-1

Cited by 10 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve DMOPs, there are various kinds of DMOEAs in the literature, which can be categorized as follows: diversity approach [ 19 , 20 , 21 ], memory mechanism [ 22 , 23 , 24 ], and prediction-based method [ 25 , 26 , 27 ].…”

Section: Preliminary Studies and Related Workmentioning

confidence: 99%

Dynamic Multi-Objective Optimization in Brazier-Type Gasification and Carbonization Furnace

Zhang

et al. 2023

Materials

View full text Add to dashboard Cite

With the special porous structure and super-long carbon sequestration characteristic, the biochar has shown to have potential in improving soil fertility, reducing carbon emissions and increasing soil carbon sequestration. However, the biochar technology has not been applied on a large scale, due to the complex structure, long transportation distance of raw materials, and high cost. To overcome these issues, the brazier-type gasification and carbonization furnace is designed to carry out dry distillation, anaerobic carbonization and have a high carbonization rate under high-temperature conditions. To improve the operation and maintenance efficiency, we formulate the operation of the brazier-type gasification and carbonization furnace as a dynamic multi-objective optimization problem (DMOP). Firstly, we analyze the dynamic factors in the work process of the brazier-type gasification and carbonization furnace, such as the equipment capacity, the operating conditions, and the biomass treated by the furnace. Afterward, we select the biochar yield and carbon monoxide emission as the dynamic objectives and model the DMOP. Finally, we apply three dynamic multiobjective evolutionary algorithms to solve the optimization problem so as to verify the effectiveness of the dynamic optimization approach in the gasification and carbonization furnace.

show abstract

Section: Preliminary Studies and Related Workmentioning

confidence: 99%

Dynamic Multi-Objective Optimization in Brazier-Type Gasification and Carbonization Furnace

Zhang

et al. 2023

Materials

View full text Add to dashboard Cite

show abstract

“…In the literature, various change response strategies have been proposed to track the POS of the new environment quickly by initializing the population and respond to the changed environment in time, which are the core component of DMOEAs. Generally, they can be mainly classified as follows: diversity approaches [13][14][15], memory mechanisms [16][17][18], and prediction-based methods [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

“…To solve DMOPs, there are various kinds of dynamic MOEAs (DMOEAs) in the literature, which can be categorized as follows: diversity approaches [13][14][15], memory mechanisms [16][17][18], and prediction-based methods [19][20][21]. Generally, the diversity approaches include increasing diversity [22], maintaining diversity [15], and multi-population strategy [23].…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Regression Transfer Learning for Dynamic Multi-Objective Optimization

2023

View full text Add to dashboard Cite

Many multi-objective optimization problems in the real world have conflicting objectives, and these objectives change over time, known as dynamic multi-objective optimization problems (DMOPs). In recent years, transfer learning has attracted growing attention to solve DMOPs, since it is capable of leveraging historical information to guide the evolutionary search. However, there is still much room for improvement in the transfer effect and the computational efficiency. In this paper, we propose a cluster-based regression transfer learning-based dynamic multi-objective evolutionary algorithm named CRTL-DMOEA. It consists of two components, which are the cluster-based selection and cluster-based regression transfer. In particular, once a change occurs, we employ a cluster-based selection mechanism to partition the previous Pareto optimal solutions and find the clustering centroids, which are then fed into autoregression prediction model. Afterwards, to improve the prediction accuracy, we build a strong regression transfer model based on TrAdaboost.R2 by taking advantage of the clustering centroids. Finally, a high-quality initial population for the new environment is predicted with the regression transfer model. Through a comparison with some chosen state-of-the-art algorithms, the experimental results demonstrate that the proposed CRTL-DMOEA is capable of improving the performance of dynamic optimization on different test problems.

show abstract

“…The first version is called DNSGA-II-A, which introduces randomly generated solutions to replace part of the population; the second version is called DNSGA-II-B, which enhances the diversity by replacing a portion of the population with mutated solutions. Liu et al [14] proposed a method sensitive to change intensity. When environmental change is detected, two strategies are utilized in different situations: an inverse modeling is used for drastic changes, while partially initialization is utilized for mild ones.…”

Section: B Related Workmentioning

confidence: 99%

An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

Xu¹,

Jiang²,

Hu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this paper, a novel prediction algorithm based on incremental support vector machine (ISVM) is proposed, called ISVM-DMOEA. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an incremental support vector machine without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.

show abstract

A diversity introduction strategy based on change intensity for evolutionary dynamic multiobjective optimization

Cited by 10 publications

References 31 publications

Dynamic Multi-Objective Optimization in Brazier-Type Gasification and Carbonization Furnace

Dynamic Multi-Objective Optimization in Brazier-Type Gasification and Carbonization Furnace

Cluster-Based Regression Transfer Learning for Dynamic Multi-Objective Optimization

An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

Contact Info

Product

Resources

About