A modified hybrid rice optimization algorithm for solving 0-1 knapsack problem

Shu, Zhe; Ye, Zhiwei; Zong, Xinlu; Liu, Shiqin; Zhang, Daode; Wang, Chunzhi; Wang, Mingwei

doi:10.1007/s10489-021-02717-4

Cited by 30 publications

(16 citation statements)

References 33 publications

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“…Step 4: Update the safe area of the algorithm by equations ( 4) to (6), and recalculate the fitness values of all individuals.…”

Section: Beginmentioning

confidence: 99%

“…SD(x d ) is the standard deviation of the population, x best is the current optimal solution. If lb d and ub d exceed the lower and upper limit of the solution space, set to the original ub d and lb d The area update threshold D is updated adaptively, as shown in equation (6).…”

Section: Battle Royale Optimization Algorithmmentioning

confidence: 99%

“…It has been widely used in solving various optimization problems. 5,6 Among them, particle swarm optimization algorithm (PSO), 7 fruit fly optimization algorithm (FOA), 8 CSO algorithm, 9 ant lion optimization algorithm (ALO), 10 whale optimization algorithm (WOA), 11 and other algorithms have shown excellent and unique optimization capabilities.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Zhang

Song

et al. 2022

Advances in Mechanical Engineering

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To advance the calculation performance of the battle royale optimization algorithm (BRO), a hybrid improved BRO algorithm (HBC) is proposed in this paper. The level mechanism of the chicken swarm optimization algorithm (CSO) is integrated into the BRO algorithm to divide all into elite players and ordinary players, and the level relationship of different players is established. Then, an elite player update method of random exploring and directional update in a small range is proposed to improve the development ability. The update method of ordinary players improved, the update mechanism of elite random guidance is introduced to make full use of the excellent location information in the population. The performance verification experiment of the HBC algorithm is carried out on 20 benchmark functions and a practical project. Comparing with several other algorithms, the computational performance of the HBC algorithm is the best. Furthermore, the HBC algorithm is applied to solve the inverse kinematics of the 7R 6DOF robot. The experimental results show that the HBC algorithm effectively improves the average convergence accuracy and reduces the running time, compared with the BRO algorithm. This fully shows that the HBC algorithm is more competitive in stability, calculation accuracy, and speed.

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“…Step 4: Update the safe area of the algorithm by equations ( 4) to (6), and recalculate the fitness values of all individuals.…”

Section: Beginmentioning

confidence: 99%

Section: Battle Royale Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Zhang

Song

et al. 2022

Advances in Mechanical Engineering

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“…For example, Liu et al [36] presented an image segmentation method that used HRO to find the fittest multi-level thresholds by using Renyi's entropy as the fitness function, and experiments proved that HRO prevailed over the other six commonly used evolutionary algorithms on most metrics. Su et al [37] designed two different hybrid models for the complex large-scale 0-1 knapsack problem by using novel combinations of improved HRO and binary ant colony algorithm, which achieved better performance on different size datasets. In addition, Ye et al [38] regarded the band selection problem as a combinatorial optimization problem and employed binary HRO to select the optimal band set for HSI, which obtained good results in classification precision and execution efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Cai

Liu

et al. 2022

Symmetry

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Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.

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“…The breeding mechanism of hybrid rice is similar to the idea of a metaheuristic algorithm to obtain optimal solutions, which can be abstracted as a mathematical model. Hybrid rice optimization algorithm (HRO), 37 based on breeding mechanisms, is widely used to solve various combinatorial optimization problems, such as 0-1 knapsack problem 38 and feature selection, 39 etc. In particular, Liu et al 40 proposed a multilevel thresholding technique based on HRO, which segmented cement hydrate from the cement images.…”

Section: Introductionmentioning

confidence: 99%

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

Song

et al. 2022

Int J Imaging Syst Tech

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In modern clinical diagnostics, magnetic resonance imaging (MRI) is frequently used for brain tumor detection because of its high resolution of soft tissues, which plays a crucial role in the prevention, detection, and treatment planning. Therefore, it is meaningful to obtain high‐quality MR images by automatic thresholding for aiding diagnosis. Most multilevel thresholding techniques are based on histograms. It is susceptible to the limitation of grayscale spatial distribution and is difficult to be used for MR images with variable and complex morphology. In this paper, a novel multilevel thresholding segmentation approach with a non‐histogram using a modified threshold score (MTS) is proposed. An opposition‐based learning hybrid rice optimization (OHRO) algorithm is used to reduce the computational cost of MTS for the purpose of optimizing the threshold search. The strategy of opposition‐based learning expands the space of feasible solutions and avoids the search from stalling. The proposed approach is evaluated through the Harvard Medical School's whole brain atlas dataset. Comparing the results with TS‐OHRO, Tsallis‐OHRO, Kapur‐OHRO, and Masi‐OHRO, MTS‐OHRO achieves better quantitative and qualitative outcomes which demonstrate that the application of MTS‐OHRO to MR images is effective and feasible.

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A modified hybrid rice optimization algorithm for solving 0-1 knapsack problem

Cited by 30 publications

References 33 publications

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

A modified threshold score‐based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition‐based learning hybrid rice optimization algorithm

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