A framework of scalable dynamic test problems for dynamic multi-objective optimization

Jiang, Shouyong; Yang, Shengxiang

doi:10.1109/cidue.2014.7007864

Cited by 20 publications

(11 citation statements)

References 16 publications

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“…Recently, benchmark generators for continuous dynamic constrained optimization [77,78,26,14] and continuous dynamic multiobjective optimization [25,61,79,80,81,82,83,84,85] are proposed. But, constrained and multi-objective optimization under the discrete space has not attracted much attention yet and deserves future consideration.…”

Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

Self Cite

472

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: The Generation Of Dynamicsmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

Self Cite

472

213

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“…According to [31], Jiang and Yang has pointed out that the dynamic nature in the decision variable space can be captured by the function vector, S, in a DMOP model and S(x I ) = S(x II − g(x I )) . Therefore, if we make some modifications of g(x I ), we can obtain various changing patterns of the PS.…”

Section: B Ps Center-based Prediction Methodsmentioning

confidence: 99%

Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems

Miao

Gong

Zhang

et al. 2019

IEEE Trans. Cybern.

171

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Various real-world multiobjective optimization problems are dynamic, requiring evolutionary algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set (PS) so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of EAs in solving a dynamic multiobjective optimization problem (DMOP). To more accurately predict the moving location of the PS, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the intensity of the environmental change. To examine the performance of the developed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular EAs for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs.

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“…Besides, dCOEA uses an additional external population to store useful but outdated archived solutions, hoping to help the evolving population quickly adapt to the new environment by exploiting these history information. It has been shown that dCOEA is very promising for handling dynamic environments [18], [24]. 4) PPS: It is a representative of prediction-based methods that model the movement track of the POF or POS in dynamic environments and then use this model to predict the new location of POS.…”

Section: B Compared Algorithmsmentioning

confidence: 99%

A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

Jiang

Yang

2017

IEEE Trans. Evol. Computat.

Self Cite

266

157

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Newcastle University ePrints -eprint.ncl.ac.uk Jiang S, Yang S. A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 2017, 21(1), 65-82.Abstract-This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective optimization, the proposed algorithm detects environmental changes and responds to them in a steady-state manner. If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous environments and the new environment. This way, the algorithm can quickly adapt to changing environments and thus is expected to provide a good tracking ability. The proposed algorithm is tested on a number of bi-and three-objective benchmark problems with different dynamic characteristics and difficulties. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization in comparison with state-of-the-art methods.Index Terms-Change detection, change response, dynamic multiobjective optimization, steady-state and generational evolutionary algorithm.

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A framework of scalable dynamic test problems for dynamic multi-objective optimization

Cited by 20 publications

References 16 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Multidirectional Prediction Approach for Dynamic Multiobjective Optimization Problems

A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

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