Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem

Wen, Changsheng; Jia, Heming; Wu, Di; Rao, Honghua; Li, Shanglong; Liu, Qingxin; Abualigah, Laith

doi:10.3390/math10193604

Cited by 32 publications

(16 citation statements)

References 59 publications

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“…One unique approach to solving optimization issues is the combination of the ROA [ 36 , 37 ], a hybrid technique, and CSCO in feature selection. The proposed strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure.…”

Section: Methodsmentioning

confidence: 99%

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

Alhassan

2024

PLoS ONE

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Breast cancer is one of the most often diagnosed cancers in women, and identifying breast cancer histological images is an essential challenge in automated pathology analysis. According to research, the global BrC is around 12% of all cancer cases. Furthermore, around 25% of women suffer from BrC. Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated in this work the viability of automatically identifying and classifying BrC. The first stage is pre-processing, which employs an Adaptive Switching Modified Decision Based Unsymmetrical Trimmed Median Filter (ASMDBUTMF) to remove high-density noise. After the image has been pre-processed, it is segmented using the Thresholding Level set approach. Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. The suggested strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure. Additionally, it aids in resolving problems pertaining to global optimization. Following the selection, the best characteristics proceed to the categorization procedure. A DL classifier called the Conditional Variation Autoencoder is used to discriminate between cancerous and benign tumors while categorizing them. Consequently, a classification accuracy of 99.4%, Precision of 99.2%, Recall of 99.1%, F- score of 99%, Specificity of 99.14%, FDR of 0.54, FNR of 0.001, FPR of 0.002, MCC of 0.98 and NPV of 0.99 were obtained using the proposed approach. Furthermore, compared to other research using the current BreakHis dataset, the results of our research are more desirable.

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Section: Methodsmentioning

confidence: 99%

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

Alhassan

2024

PLoS ONE

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“…When the mucus cannot find food at this location for a long time, it means that the nutrients in the area are no longer sufficient to support the continued survival of the slime molds, so the slime molds in the area need to be relocated. The restart scheme [ 9 ] can help poorer individuals transition from a local optimal state to other positions, so we use a restart strategy here to change the position of poorer individuals. In this strategy, we use the trial vector trial(i) to record the number of times the position has not improved.…”

Section: Hybrid Improvement Strategymentioning

confidence: 99%

“…Using F23 functions for validation is not sufficient. We have added the CEC2020 test function [ 9 ] to verify this. In this experiment, we set the variables as N = 30, T = 500, and dim = 10.…”

Section: Experimental Partmentioning

confidence: 99%

“…NFL’s law motivates researchers to enhance their ability to solve new problems by improving currently known algorithms. For example, Chen et al were inspired by the lifestyle of beluga whales and developed an IBWO [ 8 ] that improved the algorithm’s global optimization ability; Wen et al enhanced the global optimization capability of the algorithm by using a new host-switching mechanism [ 9 ]; Wu et al improved the sand cat’s wandering strategy and applied it to engineering problems [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation

Chen,

Wang,

Jia

et al. 2023

Biomimetics

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The slime mold algorithm (SMA) and the arithmetic optimization algorithm (AOA) are two novel meta-heuristic optimization algorithms. Among them, the slime mold algorithm has a strong global search ability. Still, the oscillation effect in the later iteration stage is weak, making it difficult to find the optimal position in complex functions. The arithmetic optimization algorithm utilizes multiplication and division operators for position updates, which have strong randomness and good convergence ability. For the above, this paper integrates the two algorithms and adds a random central solution strategy, a mutation strategy, and a restart strategy. A hybrid slime mold and arithmetic optimization algorithm with random center learning and restart mutation (RCLSMAOA) is proposed. The improved algorithm retains the position update formula of the slime mold algorithm in the global exploration section. It replaces the convergence stage of the slime mold algorithm with the multiplication and division algorithm in the local exploitation stage. At the same time, the stochastic center learning strategy is adopted to improve the global search efficiency and the diversity of the algorithm population. In addition, the restart strategy and mutation strategy are also used to improve the convergence accuracy of the algorithm and enhance the later optimization ability. In comparison experiments, different kinds of test functions are used to test the specific performance of the improvement algorithm. We determine the final performance of the algorithm by analyzing experimental data and convergence images, using the Wilcoxon rank sum test and Friedman test. The experimental results show that the improvement algorithm, which combines the slime mold algorithm and arithmetic optimization algorithm, is effective. Finally, the specific performance of the improvement algorithm on practical engineering problems was evaluated.

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“…Where T s and T h are integers of 0.625 and R and L are continuous variables. The specific constraints are referred to in [43]. The schematic diagram of optimal structure design is shown in Figure 9.…”

Section: Pressure Vessel Design Problemmentioning

confidence: 99%

A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Rao

Jia

et al. 2022

Mathematics

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The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results.

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Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem

Cited by 32 publications

References 59 publications

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm

Hybrid Slime Mold and Arithmetic Optimization Algorithm with Random Center Learning and Restart Mutation

A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

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