Adaptive Distributed Differential Evolution

Zhan, Zhi-Hui; Wang, Zi-Jia; Jin, Hu; Zhang, Mengjie

doi:10.1109/tcyb.2019.2944873

Cited by 206 publications

(54 citation statements)

References 54 publications

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“…In the literature, researchers have found that the exploration and exploitation abilities of EC algorithms can be adaptively adjusted according to the evolutionary state [50]- [52]. In this sense, clustering analysis has been widely used to estimate the evolutionary state and the parameters of EC algorithms can be adjusted adaptively.…”

Section: Aglsmentioning

confidence: 99%

Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization

Wang

Zhan

Kwong

et al. 2021

IEEE Trans. Cybern.

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160

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Large-scale optimization has become a significant and challenging research topic in the evolutionary computation (EC) community. Although many improved EC algorithms have been proposed for large-scale optimization, the slow convergence in the huge search space and the trap into local optima among massive suboptima are still the challenges. Targeted to these two issues, this article proposes an adaptive granularity learning distributed particle swarm optimization (AGLDPSO) with the help of machine-learning techniques, including clustering analysis based on locality-sensitive hashing (LSH) and adaptive granularity control based on logistic regression (LR). In AGLDPSO, a master-slave multisubpopulation distributed model is adopted, where the entire population is divided into multiple subpopulations, and these subpopulations are co-evolved. Compared with other large-scale optimization algorithms with single population evolution or centralized mechanism, the multisubpopulation distributed co-evolution mechanism will fully exchange the evolutionary information among different subpopulations to further enhance the population diversity. Furthermore, we propose an adaptive granularity learning strategy (AGLS) based on LSH and LR. The AGLS is helpful to determine an appropriate subpopulation size to control the learning granularity of the distributed subpopulations in different evolutionary states to balance the exploration ability for escaping from massive suboptima and the exploitation ability for converging in the huge search space. The experimental results Manuscript

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Section: Aglsmentioning

confidence: 99%

Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization

Wang

Zhan

Kwong

et al. 2021

IEEE Trans. Cybern.

Self Cite

160

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“…First, evolutionary algorithms (EAs) are efficient tools for tackling optimization problems with different properties and challenges, such as large scale [5]- [7], dynamic [8], multimodal [9]- [11], multiobjective [12]- [14], and many objective [15]. Second, there is an increasing number of real-world optimizations requiring distributed approaches [16], [17] and data-driven approaches [18], because their objective functions (and/or constraints functions) are always expensive, computationally intensive, or time consuming to perform. That is, evaluating the fitness (i.e., quality) of candidate solutions can be unaffordable in such real-world application problems [19].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Zhan

Wang

et al. 2021

IEEE Trans. Cybern.

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Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses. For the experimental comparisons, a specific DDEA-PES algorithm is developed as an instance by adopting a genetic algorithm as the optimizer and radial basis function neural networks as the base models. The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs. Moreover, when compared with traditional nondata-driven methods, the proposed DDEA-PES algorithm only requires about 2% computational budgets to produce competitive results.

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“…Liu et al developed a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation (HHDE) [42]. Zhan et al proposed an adaptive DDE (ADDE) [43] to relieve the sensitivity of strategies and parameters. Chen et al developed a distributed individual differential evolution (DIDE) algorithm [44] based on a distributed individual for multiple peaks (DIMPs) framework and two novel mechanisms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

2021

Mobile Information Systems

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Particle swarm optimization (PSO) is a common metaheuristic algorithm. However, when dealing with practical engineering structure optimization problems, it is prone to premature convergence during the search process and falls into a local optimum. To strengthen its performance, combining several ideas of the differential evolution algorithm (DE), a dynamic probability mutation particle swarm optimization with chaotic inertia weight (CWDEPSO) is proposed. The main improvements are achieved by improving the parameters and algorithm mechanism in this paper. The former proposes a novel inverse tangent chaotic inertia weight and sine learning factors. Besides, the scaling factor and crossover probability are improved by random distributions, respectively. The latter introduces a monitoring mechanism. By monitoring the convergence of PSO, a developed mutation operator with a more reliable local search capability is adopted and increases population diversity to help PSO escape from the local optimum effectively. To evaluate the effectiveness of the CWDEPSO algorithm, 24 benchmark functions and two groups of engineering optimization experiments are used for numerical and engineering optimization, respectively. The results indicate CWDEPSO offers better convergence accuracy and speed compared with some well-known metaheuristic algorithms.

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Adaptive Distributed Differential Evolution

Cited by 206 publications

References 54 publications

Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization

Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

A Hybrid Dynamic Probability Mutation Particle Swarm Optimization for Engineering Structure Design

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