A hybrid whale optimization algorithm with artificial bee colony

Tang, Chenjun; Sun, Wei; Xue, Min; Zhang, Xing; Tang, Hongwei; Wu, Wei

doi:10.1007/s00500-021-06623-2

Cited by 31 publications

(8 citation statements)

References 47 publications

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“…To estimate the differences between CAGWOA and other WOA variants, in this section compared CAGWOA with seven other WOA variants and the original WOA. Include: the improved WOA with backward learning (OBWOA) 108 , the improved WOA fusing Lévy flight strategy and quadratic interpolation method (MWOA) 109 , the improved WOA with Lévy flight (LWOA) 110 , the WOA with artificial swarm hybrid (ACWOA) 111 , improved WOA with chaotic local search (BWOA) 112 , improved WOA based on chaotic initialization strategy, Gaussian variation and chaotic local search strategy (CCMWOA) 113 and improved WOA based on learning (BMWOA) 114 .…”

Section: Benchmark Experimental Results and Discussionmentioning

confidence: 99%

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Wang,

Zhao,

Heidari

et al. 2024

Sci Rep

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Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

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Section: Benchmark Experimental Results and Discussionmentioning

confidence: 99%

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Wang,

Zhao,

Heidari

et al. 2024

Sci Rep

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show abstract

“…In equation ( 3 h  all show a linear self-loop relationship. This linear self-recycling relationship implies that there is a direct dependency between the current state and the previous state in the GRU network, i.e., the current state is affected by the previous state, and this effect is transmitted through a linear function [20]. After integrating the FG, IG as well as the OG, the formula for the reset gate calculation in the GRU network structure is shown in equation (5).…”

Section: Gru-based Charging Pile Fault Alarm Modeling Studymentioning

confidence: 99%

Charging Pile Fault Prediction Model Based on GRU Network and WOA

Yang,

Fang,

Serrano

et al. 2024

JAIT

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The global energy structure is transforming, and new energy vehicles are becoming the future of the automobile industry. However, the development of charging piles and related facilities has not kept pace with the growth of new energy vehicles. This study uses the gated recurrent unit network and the whale algorithm to construct a high-performance charging pile fault prediction model. The proposed model, which utilizes the whale algorithm to prevent the gated recurrent unit network from falling into local optima, demonstrates improved predictive information extraction and prediction ability. The experimentally verified results indicate that the proposed model achieved 92.02% prediction accuracy, 85.66% recall, and 93.87% F1 value. Additionally, the proposed model demonstrates excellent computational ability with an average running time of under 5 minutes on both datasets. This result is a substantial reduction from the control model's running time. The experimental findings show that the study's suggested model has a good ability to anticipate fault data. Its sophistication is verified by comparative tests, which can provide a reference for subsequent research.

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“…The results prove that the HWMWOA can overcome the poor population diversity and premature convergence of WOA. Tang et al [ 213 ] proposed a hybrid WOA with an artificial bee colony (ACWOA) to solve global optimization problems. The ACWOA adds non-linear convergence factors and adaptive inertia weight coefficients to the movement phase of the whales to accelerate the convergence rate and chaotic map technique to the population initialization phase to avoid local optima trapping.…”

Section: Hybrid Variants Of Woamentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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A hybrid whale optimization algorithm with artificial bee colony

Cited by 31 publications

References 47 publications

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Charging Pile Fault Prediction Model Based on GRU Network and WOA

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

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