An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems

Li, Chunquan; Luo, Zu; Song, Zhenshou; Feng, Yang; Jiang, Fan; Liu, Peter

doi:10.1109/access.2019.2900486

Cited by 22 publications

(11 citation statements)

References 64 publications

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“…( 2019 ) SCA + BSO EBS-SCA Global optimization problems EBS-SCA yields better performance compared to other meta-heuristic algorithms in terms of global search ability and convergence speed Li et al. ( 2019 ) BSO_SCA – Benchmark functions BSO_SCA has considerable merit Li et al. ( 2020 ) SCA + SE SCA-SE – Continuous Optimization Problems Effectiveness of SCA-SE compared to other optimization approaches Cai et al.…”

Section: Recent Variants Of the Sine Cosine Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

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Section: Recent Variants Of the Sine Cosine Algorithmmentioning

confidence: 99%

A comprehensive survey of sine cosine algorithm: variants and applications

et al. 2021

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“…Zhan et al [14] proposed a simple grouping strategy to reduce the clustering process's computational complexity, and a new creating operator to generate high-quality idea. Cao et al [15] and Li et al [16], [17] applied a randomly clustering approach, which evenly partition the current ideas and new ideas, and they also proposed the new creating operators to better trade off the algorithm's exploitation and exploration. Zhou et al [18] not only utilized the random grouping strategy, but also used a dynamic step-size schedule based creating operator.…”

Section: Related Work a Brain Storm Algorithmmentioning

confidence: 99%

A Compact Brain Storm Algorithm for Matching Ontologies

Xue

2020

IEEE Access

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An ontology can formally present the domain knowledge by specifying the domain concepts and their relationships, which is a kernel technique for addressing the data heterogeneity issue in the semantic web. However, since existing ontologies are developed and maintained independently by different communities, a concept and its relationship with the others could be described in different ways, yielding the ontology heterogeneity problem. To solve this problem, in this work, we formally construct an optimal model for it, and propose a similarity measure for distinguishing identical ontology entities. Since determining the high-quality ontology alignment is a complex process, we propose to utilize a Brain Storm Optimization algorithm (BSO) to optimize the alignment. BSO is a recently developed Swarm Intelligence algorithm (SI), which can effectively solve the complex optimization problem by imitating the human's idea generating process. However, classic BSO needs to cluster various ideas in each generation and carry out the evolving operators on all ideas, which increases the computational complexity. To improve the efficiency of BSO-based ontology matcher, a Compact BSO (CBSO) is further proposed, which can reduce the memory consumption by utilizing the probabilistic representation on the idea cluster, and improve the algorithm's speed through the compact crossover operator and perturbation operator. The experiment uses the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our approach's performance. The comparisons among the state-of-the-art ontology matchers and our proposal show that CBSO-based ontology matcher can efficiently determine high-quality ontology alignments.INDEX TERMS Ontology matching, compact brain storm optimization algorithm, ontology alignment evaluation initiative.

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“…After the initial migration process, G 5 should obtain some solutions from the other groups, while the remainder of the solutions is generated at random using (1). The solutions that are migrated from the four groups are sorted earlier, and only the top solutions are selected for the creation of G 5 : (12) where ↑ represents the sorted solutions from best to worst. Mig-CFA improvement has two stages.…”

Section: Proposed Approach: Migration-based Cuttlefish Algorithmmentioning

confidence: 99%

“…The solutions diversity in the population has also been investigated recently by other researchers [11]- [13], [60], where improving the solutions diversity issue is examined with brain storm optimization (BSO). The tests were carried out by applying various strategies such as vector grouping learning scheme [13]; enhancement of the BSO ideas grouping and ideas generation mechanism [12]; multi-information interaction [11]; and simple individual updating [60]. The authors of [14] proposed a differential evolution flame generation strategy in an original moth-flame optimization algorithm (MFO) to obtain a sufficient population diversity and improve the algorithm's exploration performance.…”

Section: Introductionmentioning

confidence: 99%

A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems

2020

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Cuttlefish algorithm (CFA) is a metaheuristic bio-inspired algorithm that mimics the color-changing behavior by the cuttlefish. It is produced by light reflected from different layers of cells and involves two processes, i.e., reflection and visibility. The reflection process simulates the light reflection mechanism, while the visibility process simulates the visible appearance of the matching pattern used by the cuttlefish. There is no cooperation strategy between the solutions of the CFA's sub-populations. The strategy can improve the capabilities of local exploitation and global exploration in terms of solution diversity and quality during the search process. This paper introduces two schemes to improve the performance of the cuttlefish algorithm in continuous optimization problems. Firstly, a migration strategy is employed between the multi-population cuttlefish to increase solutions diversity during the search process. Secondly, one of the exploitation strategies of the standard cuttlefish is replaced with a new exploitation strategy based on short-term memory. The test demonstrates that the proposed algorithm outperforms the standard cuttlefish algorithm. Besides, the performance of the proposed algorithm was investigated using the CEC2013 benchmarking test functions. Comparisons with several state-of-the-art algorithms were performed, and the outcomes indicated that the proposed method offers a competitive performance advantage over the alternatives.

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An Enhanced Brain Storm Sine Cosine Algorithm for Global Optimization Problems

Cited by 22 publications

References 64 publications

A comprehensive survey of sine cosine algorithm: variants and applications

A comprehensive survey of sine cosine algorithm: variants and applications

A Compact Brain Storm Algorithm for Matching Ontologies

A Migration-Based Cuttlefish Algorithm With Short-Term Memory for Optimization Problems

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