Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

Chen, Zong-Gan; Zhan, Zhi-Hui; Wang, Hua; Zhang, Mengjie

doi:10.1109/tevc.2019.2944180

Cited by 109 publications

(51 citation statements)

References 42 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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119

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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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119

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“…Some of the most widely used algorithms for text classification in machine learning are Logistic regression, Naïve Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Random Forest. All these disparate algorithms may suit different sorts of problem sets, the performance of the aforestated classifiers rely heavily on the feature engineering process [22]. The relevance and quality of the feature extracted are directly proportional to the performance of an algorithm.…”

Section: Background a Automatic Text Classificationmentioning

confidence: 99%

Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Singh

Subramani

et al. 2020

IEEE Access

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Social Media has become an integral part of our daily life. Not only it enables collaboration and flow of information but has also become an imperative tool for businesses and governments around the world. All this makes a compelling case for everyone to be on some sort of online social media platform. However, this virtuousness is overshadowed by some of its shortcomings. The manifestation of antisocial behaviour online is a growing concern that hinders participation and cultivates numerous social problems. Antisocial behaviour exists in its various forms such as aggression, disregard for safety, lack of remorse, unlawful behaviour, etc. The paper introduces a deep learning-based approach to detect and classify online antisocial behaviour (ASB). The automatic content classification addresses the issue of scalability, which is imperative when dealing with online platforms. A benchmark dataset was created with multi-class annotation under the supervision of a domain expert. Extensive experiments were conducted with multiple deep learning algorithms and their superior results were validated against the results from the traditional machine learning algorithms. Visually enhanced interpretation of the classification process is presented for model and error analyses. Accuracy of up to 99% in class identification was achieved on the ground truth dataset for empirical validation. The study is an evidence of how the cutting-edge deep learning technology can be utilized to solve a real-world problem of curtailing antisocial behaviour, which is a public health threat and a social problem.

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“…Zhao et al [51] borrowed the local binary operator idea in image processing to help efficiently form the niches. Chen et al [52] designed a distributed individual DE that treated each individual as a distributed niche to track peaks. These algorithms all use DE as the base algorithm to solve MMOPs and achieve a great success.…”

Section: ) Ga For Mmopsmentioning

confidence: 99%

“…In this paper, four accuracy levels () that  = 1E−01,  = 1E−02,  = 1E−03, and  = 1E−04 are adopted in the experiments. The results of  = 1E−04 are mainly reported as [49]- [52]. Besides, the NP, MaxFEs, and K of MaHDE adopt the same settings as [51].…”

Section: A Test Functions and Experimental Settingsmentioning

confidence: 99%

A Multi-Angle Hierarchical Differential Evolution Approach for Multimodal Optimization Problems

et al. 2020

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Multimodal optimization problem (MMOP) is one of the most common problems in engineering practices that requires multiple optimal solutions to be located simultaneously. An efficient algorithm for solving MMOPs should balance the diversity and convergence of the population, so that the global optimal solutions can be located as many as possible. However, most of existing algorithms are easy to be trapped into local peaks and cannot provide high-quality solutions. To better deal with MMOPs, considerations on the solution quality angle and the evolution stage angle are both taken into account in this paper and a multi-angle hierarchical differential evolution (MaHDE) algorithm is proposed. Firstly, a fitness hierarchical mutation (FHM) strategy is designed to balance the exploration and exploitation ability of different individuals. In the FHM strategy, the individuals are divided into two levels (i.e., low/high-level) according to their solution quality in the current niche. Then, the low/high-level individuals are applied to different guiding strategies. Secondly, a directed global search (DGS) strategy is introduced for the lowlevel individuals in the late evolution stage, which can improve the population diversity and provide these low-level individuals with the opportunity to research the global peaks. Thirdly, an elite local search (ELS) strategy is designed for the high-level individuals in the late evolution stage to refine their solution accuracy. Extensive experiments are developed to verify the performance of MaHDE on the widely used MMOPs test functions i.e., CEC'2013. Experimental results show that MaHDE generally outperforms the compared state-of-the-art multimodal algorithms. INDEX TERMS Multi-angle hierarchical, two-stage search, differential evolution, multimodal optimization problems I. INTRODUCTION Multimodal optimization problems (MMOPs), as one kind of challenging and interesting optimization problems, have attracted increasing attentions in recent years [1]−[4]. MMOPs are an important problem area as it widely exists in many real-world applications [5], such as virtual camera composition problems [6], metabolic network modeling problems [7], laser pulse shaping problems [8], job scheduling problems [9][10], and neutral network problems [11]. These MMOPs are required the algorithms to locate the global peaks as many as possible and refine the accuracy of the found solutions as high as possible, so that the high-quality decisions can be finally made. In detail, MMOP is a kind of complex optimization problem that requires the algorithm to not only locate multiple global peaks simultaneously, but also achieve certain accuracy of solutions on the global peaks. In fact, the algorithm for MMOPs faces the problem of how to improve the

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Distributed Individuals for Multiple Peaks: A Novel Differential Evolution for Multimodal Optimization Problems

Cited by 109 publications

References 42 publications

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

A Multi-Angle Hierarchical Differential Evolution Approach for Multimodal Optimization Problems

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