Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution

Wang, Minghao; Ma, Yongjie; Wang, Peidi

doi:10.1016/j.ins.2022.06.040

Cited by 30 publications

(12 citation statements)

References 34 publications

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“…The solution quality is significantly influenced by the mutation strategy, as it directly generates the offsprings [39]. Specifically, the mutation strategy is essential to preserving population diversity and exploring the search regions with diverse properties.…”

Section: Probability-driven Multi-strategy Mutationmentioning

confidence: 99%

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

Tang,

Du,

et al. 2024

Preprint

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The influence maximization is regarded as one of the most pivotal concerns in social network analysis especially in the inevitable trend that more and more individuals are being involved into the global networked society. The purpose of the problem is to identify a set of influential nodes from the social network and activate them to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, the exploration and exploitation operations need further investigated according to the iterative information of the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. To provide more adequate seed set by utilizing the differential evolution, the mutation in the framework, consisted by three policies, namely particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum based on a potential substitution pool consisting of structural hole nodes. Experimental results on six real-world social networks demonstrate that the proposed algorithm shows competitive performance in terms of both efficacy and efficiency when compared to the state-of-the-art algorithms.

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Section: Probability-driven Multi-strategy Mutationmentioning

confidence: 99%

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

Tang,

Du,

et al. 2024

Preprint

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“…Hybrid coding techniques [31], [32] were introduced into the AADE algorithm to create the hybrid coding AADE algorithm, which was then used to solve the simultaneous dynamic optimization model of technical indicators and mining sequence. The basic steps of the optimization algorithm are as follows [33], [34], [35], [36], [37].…”

Section: ) Hybrid Coding Of Aade To Solve the Modelmentioning

confidence: 99%

Simultaneous Dynamic Optimization of Technical Indicators and Mining Sequence in Metal Mines Using Hybrid Coding AADE

Wang,

2023

IEEE Access

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Technical indicators and mining sequence are often optimized separately when optimizing metal mine production. This approach ignores the mutual influence between indicators and mining sequence, and the optimization results may not reach the global optimum. This study focuses on the entire production process of a metal mine and establishes a model and creates an algorithm for the simultaneous dynamic optimization of the technical indicators and mining sequence of a metal mine. A model of the dynamic relationships between technical indicators is initially created in order to derive an optimization model that dynamically optimizes both the technical indicators and the mining sequence of a metal mine. A hybrid coding AADE algorithm is developed to solve the optimization equation. A test case of the Huogeqi copper mine is presented to demonstrate the use of the model and the algorithm. The simultaneous dynamic optimization of technical indicators and mining sequence produced greater NPV in the test case than the optimization of technical indicators alone; NPV increased by 1161.01 10 4 CNY. In addition, the hybrid coding AADE algorithm was compared with hybrid coding GA, DE and ADE algorithms. The hybrid coding AADE algorithm performed searches significantly better than the other three hybrid coding algorithms in solving the simultaneous dynamic optimization for technical indicators and mining sequence.

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“…Numerous self-adaptive parameter approaches are proposed in the literature [33][34][35][36] to solve different optimization problems. Algorithmic simplicity, ease of implementation, and strong convergence have inspired many researchers to employ DE to solve various optimization problems.…”

Section: The Classical Dementioning

confidence: 99%

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

Raghul,

Jeyakumar

2023

IEEE Access

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In conventional optimization problems, it is assumed that all relevant parametric constraints remain stationary. In contrast, optimization problems encountered in practical applications are dynamic and supervened by uncertainties. The research community has evinced a keen interest in multi-population approaches combined with nature-inspired algorithms to manage dynamic optimization problems efficiently. Applying multi-population approaches to solve dynamic optimization problems engenders specific vital issues, such as reproducing sub-populations in new environments influenced by archival information. Moreover, overpartitioning the population may lead to aberrant utilization of computational resources among the subpopulations. These impediments are addressed using the proposed hybrid multi-population reinitialization strategy, which is a combination of distributed differential evolution algorithmic framework and reinitialization strategy. This scheme relies on simple reinitialization to surmount the dynamism. This framework was assessed on different instances in a moving peak benchmark problem, a proven benchmarking function in the domain of dynamic optimization. Furthermore, this study encompasses a comparative and statistical analysis to validate the effectiveness of the proposed approach in comparison to cutting-edge algorithms in solving dynamic optimization problems efficiently. The experimental results consistently show that the hybrid multi-population reinitialization strategy outperforms conventional Differential Evolution algorithms across various parameter configurations. This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.

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Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution

Cited by 30 publications

References 34 publications

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

An adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies for influence maximization in social networks

Simultaneous Dynamic Optimization of Technical Indicators and Mining Sequence in Metal Mines Using Hybrid Coding AADE

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

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