A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

Liu, Ruochen; Chen, Yangyang; Ma, Wenping; Mu, Caihong; Jiao, Licheng

doi:10.1007/s00500-013-1175-7

Cited by 43 publications

(14 citation statements)

References 26 publications

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“…When a change is detected, promising dynamic handling techniques should be adopted to respond to the change. Such techniques include mainly prediction-based [17,47,26], memory-based [21,31], diversity-introduction [10,1,2], and multiple population methods [33,14,25]. Depending on the behavior of the algorithms, these techniques can be divided into two categories: techniques for accelerating convergence, and techniques for improving diversity.…”

Section: Change Responsementioning

confidence: 99%

Hybrid of memory and prediction strategies for dynamic multiobjective optimization

Liang

Zheng

Zhu

et al. 2019

Information Sciences

100

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Section: Change Responsementioning

confidence: 99%

Hybrid of memory and prediction strategies for dynamic multiobjective optimization

Liang

Zheng

Zhu

et al. 2019

Information Sciences

100

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“…Dynamic multiobjective optimization problems (DMOPs) are challenging due to the fact that multiple conflicting objectives that change over time must be optimized simultaneously [16], [17]. Evolutionary computation and swarm intelligence have been shown to be powerful methods to solve optimization problems in dynamic environments [18].…”

Section: Introductionmentioning

confidence: 99%

“…Evolutionary computation and swarm intelligence have been shown to be powerful methods to solve optimization problems in dynamic environments [18]. Among many others, coevolutionary approaches are very attractive [16], [19], [24]- [29]. Through competitivecooperative coevolution, different subpopulations separately optimize a subset of the decision variables, where the decomposition process of the optimization problem is adaptive rather than being manually designed and fixed at the beginning of the evolutionary optimization [19].…”

Section: Introductionmentioning

confidence: 99%

“…Through competitivecooperative coevolution, different subpopulations separately optimize a subset of the decision variables, where the decomposition process of the optimization problem is adaptive rather than being manually designed and fixed at the beginning of the evolutionary optimization [19]. Liu et al [16] proposed a decomposition method, where the subcomponents cooperate to evolve for better solutions. A linear regression prediction strategy was used to produce rapid responses to new changes in the environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Hence, the location of the new POS can be predicted, helping the population quickly track the moving POF. Prediction-based approaches have received massive attention because most existing benchmark DMOPs (e.g., the FDA test suite [15]) involve predictable characteristics, and studies along this direction can be referred to [22], [28], [32], [33], [36], [47], [54], and [55].…”

mentioning

confidence: 99%

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

Jiang

Yang

2017

IEEE Trans. Evol. Computat.

267

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 novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

Cited by 43 publications

References 26 publications

Hybrid of memory and prediction strategies for dynamic multiobjective optimization

Hybrid of memory and prediction strategies for dynamic multiobjective optimization

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

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

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