Dynamic Group Learning Distributed Particle Swarm Optimization for Large-Scale Optimization and Its Application in Cloud Workflow Scheduling

Wang, Zi-Jia; Zhan, Zhi-Hui; Yu, Wei; Lin, Ying; Zhang, Jie; Gu, Tianlong; Zhang, Jun

doi:10.1109/tcyb.2019.2933499

Cited by 195 publications

(58 citation statements)

References 47 publications

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“…For future work, the algorithm proposed in this article will be applied to solve problems with more complicated challenges, such as large-scale [74], multi/many-objective [75], multimodal [76], dynamics [77], and constraint [78]. Moreover, the BS and LDG will be extended to more different types of surrogate models to further study their efficiency in improving the algorithm performance.…”

Section: Discussionmentioning

confidence: 99%

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Zhan

Wang

et al. 2020

IEEE Trans. Evol. Computat.

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129

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By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties. First, many existing methods for building a single ad hoc surrogate are suitable for some special problems but may not work well on some other problems. Second, the optimization accuracy of DDEAs deteriorates if available data are not enough for building accurate surrogates, which is common in expensive optimization problems. To this end, this article proposes a novel DDEA with two efficient components. First, a boosting strategy (BS) is proposed for self-aware model managements, which can iteratively build and combine surrogates to obtain suitable surrogate models for different problems. Second, a localized data generation (LDG) method is proposed to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions. By integrating the BS and the LDG, the BDDEA-LDG algorithm is able to improve model accuracy and data quantity at the same time automatically according to the problems at hand. Besides, a tradeoff is empirically considered to strike a better balance between the effectiveness of surrogates and the time cost for building them. The experimental results show that the proposed BDDEA-LDG algorithm can generally outperform both traditional methods without surrogates and other state-of-the-art DDEA son widely used benchmarks and an arterial traffic signal timing real-world optimization problem. Furthermore, the proposed BDDEA-LDG algorithm can use only about 2% computational budgets of traditional methods for producing competitive results.

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Section: Discussionmentioning

confidence: 99%

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Zhan

Wang

et al. 2020

IEEE Trans. Evol. Computat.

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129

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“…Inspired by the idea of "divide-and-conquer," Potter and De Jong [35] designed the CC strategy. It is an effective method for large-scale optimization [14]- [16], [36]- [38]. The first step of this strategy is to divide the variables into several parts with a specific decomposition strategy, which can be regarded as the dimensionality reduction.…”

Section: Cooperative Coevolutionmentioning

confidence: 99%

Cooperative Coevolutionary Bare-Bones Particle Swarm Optimization With Function Independent Decomposition for Large-Scale Supply Chain Network Design With Uncertainties

Zhang

Zhan

et al. 2020

IEEE Trans. Cybern.

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Supply chain network design (SCND) is a complicated constrained optimization problem that plays a significant role in the business management. This article extends the SCND model to a large-scale SCND with uncertainties (LUSCND), which is more practical but also more challenging. However, it is difficult for traditional approaches to obtain the feasible solutions in the large-scale search space within the limited time. This article proposes a cooperative coevolutionary bare-bones particle swarm optimization (CCBBPSO) with function independent decomposition (FID), called CCBBPSO-FID, for a multiperiod three-echelon LUSCND problem. For the large-scale issue, binary encoding of the original model is converted to integer encoding for dimensionality reduction, and a novel FID is designed to efficiently decompose the problem. For obtaining the feasible solutions, two repair methods are designed to repair the infeasible solutions that appear frequently in the LUSCND problem. A step translation method is proposed to deal with the variables out of bounds, and a labeled reposition operator with adaptive probabilities is designed to repair the infeasible solutions that violate the constraints. Experiments are conducted on 405 instances with three different scales. The results show that CCBBPSO-FID has an evident superiority over contestant algorithms.

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“…The first thing is to determine the distance range for the trigger of interaction. Euclidean distance is considered for particles Xi and Xj as shown in Equation (9)…”

Section: B the Range Of Particle Interaction Fieldsmentioning

confidence: 99%

“…PSO was proposed by Kennedy and Eberhart in 1995 [1,2] to simulate social behaviors, representing the movement in a bird flock or fish school. Due to few or no assumptions about the optimization problems, PSO has been widely applied in many fields, such as power systems [3,4], image processing [5,6,7], and motor parameter settings [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

et al. 2020

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Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.

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Dynamic Group Learning Distributed Particle Swarm Optimization for Large-Scale Optimization and Its Application in Cloud Workflow Scheduling

Cited by 195 publications

References 47 publications

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Cooperative Coevolutionary Bare-Bones Particle Swarm Optimization With Function Independent Decomposition for Large-Scale Supply Chain Network Design With Uncertainties

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

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