Novel prediction and memory strategies for dynamic multiobjective optimization

Zhou, Peng; Zheng, Jinhua; Zou, Juan; Liu, Min

doi:10.1007/s00500-014-1433-3

Cited by 99 publications

(51 citation statements)

References 27 publications

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“…. In a dynamic environment, in some cases, when we compare the performance of different strategies, we need to analyze them at different time periods [34,37,54,57]. In this paper, 100 environmental changes were divided into three stages.…”

Section: Comparison On Performance Evaluation Resultsmentioning

confidence: 99%

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A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

Guo³

et al. 2019

Wireless Communications and Mobile Computing

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There are many issues to consider when integrating 5G networks and the Internet of things to build a future smart city, such as how to schedule resources and how to reduce costs. This has a lot to do with dynamic multiobjective optimization. In order to deal with this kind of problem, it is necessary to design a good processing strategy. Evolutionary algorithm can handle this problem well. The prediction in the dynamic environment has been the very challenging work. In the previous literature, the location and distribution of PF or PS are mostly predicted by the center point. The center point generally refers to the center point of the population in the decision space. However, the center point of the decision space cannot meet the needs of various problems. In fact, there are many points with special meanings in objective space, such as ideal point and CTI. In this paper, a hybrid prediction strategy carried through from both decision space and objective space (DOPS) is proposed to handle all kinds of optimization problems. The prediction in decision space is based on the center point. And the prediction in objective space is based on CTI. In addition, for handling the problems with periodic changes, a kind of memory method is added. Finally, to compensate for the inaccuracy of the prediction in particularly complex problems, a self-adaptive diversity maintenance method is adopted. The proposed strategy was compared with other four state-of-the-art strategies on 13 classic dynamic multiobjective optimization problems (DMOPs). The experimental results show that DOPS is effective in dynamic multiobjective optimization.

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Section: Comparison On Performance Evaluation Resultsmentioning

confidence: 99%

“…The prediction strategy based on center point is a widely used strategy [37,38,40,41,45,46,54,55]. The center point is defined as follows:…”

Section: The Prediction Strategy Based On Center Pointmentioning

confidence: 99%

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

Guo³

et al. 2019

Wireless Communications and Mobile Computing

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“…This critical learning process can be described as follows in a high abstraction level. (22) where G and Gmax are the current and maximum generations, Xij,G and SAij,G denote the j-th decision variable of individual Xi before and after critical learning at the G-th generation, respectively. AFG is a learning factor at the G-th generation, which gradually decreases over the generations from 0.8958 to 0.0896.…”

Section: ) Critical Learningmentioning

confidence: 99%

A Bio-Inspired Self-Learning Coevolutionary Dynamic Multiobjective Optimization Algorithm for Internet of Things Services

Yang

Jin

Hao

2019

IEEE Trans. Evol. Computat.

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The ultimate goal of the Internet of Things (IoT) is to provide ubiquitous services. To achieve this goal, many challenges remain to be addressed. Inspired from the cooperative mechanisms between multiple systems in the human being, this paper proposes a bio-inspired self-learning coevolutionary algorithm (BSCA) for dynamic multiobjective optimization of IoT services to reduce energy consumption and service time. BSCA consists of three layers. The first layer is composed of multiple subpopulations evolving cooperatively to obtain diverse Pareto fronts. Based on the solutions obtained by the first layer, the second layer aims to further increase the diversity of solutions. The third layer refines the solutions found in the second layer by adopting an adaptive gradient refinement search strategy and a dynamic optimization method to cope with changing concurrent multiple service requests, thereby effectively improving the accuracy of solutions. Experiments on agricultural IoT services in the presence of dynamic requests under different distributions are performed based on two service-providing strategies, i.e., single service and collaborative service. The simulation results demonstrate that BSCA performs better than four existing algorithms on IoT services, in particular for high-dimensional problems.

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“…In recent years, researchers have designed many new ways to solve DMOPs on the basis of static algorithms [10,11,13,14,16,17,18,19,20,21], such as random initialization [12,25,26,18,17], hyper mutation [25,22,15,33], memory [25,26,29,30,36,23,41], and prediction [31,32,33,34,35,36,42,48,49].…”

Section: Introductionmentioning

confidence: 99%

A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

Zou

Yang

et al. 2019

Swarm and Evolutionary Computation

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Novel prediction and memory strategies for dynamic multiobjective optimization

Cited by 99 publications

References 27 publications

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

A Bio-Inspired Self-Learning Coevolutionary Dynamic Multiobjective Optimization Algorithm for Internet of Things Services

A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

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