BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Ghosh, Kushal Kanti; Guha, Ritam; Bera, Suman Kumar; Sarkar, Ram; Mirjalili, Seyedali

doi:10.21203/rs.3.rs-28683/v1

Cited by 11 publications

(11 citation statements)

References 48 publications

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“…The first experiment studies the effect of two different transformation functions on the original EO. The second experiment introduces a comparison of the proposed IBEO with the original EO and another binary version of EO called BEO that was published in 2020 [83]. In the third experiment, a comparison between IBEO and different algorithms such as PSO, GOA, GWO, WOA, DA, and ISSA is performed.…”

Section: Results and Analysismentioning

confidence: 99%

“…Thus, the proposed IBEO is compared with the original EO and BEO. K. Ghosh proposed BEO as the first binary version of EO, which improved EO's exploitative abilities by using simulated annealing algorithm [83]. The average number of the selected features, classification accuracy, and fitness values are the most crucial comparative performance measurements in our experiment.…”

Section: ) Comparison Of Ibeo With Eo and Beomentioning

confidence: 99%

“…It is still necessary to have more optimization techniques to get additional enhanced results. To the best of the author's information, there are a few studies in the literature for the binary version of EO [83], [84]. This has motivated us in this study to propose a new binary version of EO and test its benefit in features selection problems as a binary optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

Elmanakhly¹,

Saleh²,

Rashed³

2021

IEEE Access

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In the last decade, data generated from different digital devices has posed a remarkable challenge for data representation and analysis. Because of the high-dimensional datasets and the rapid growth of data volume, a lot of challenges have been encountered in various fields such as data mining and data science. Conventional machine learning classifiers are of limited ability to handle the problems of high dimensionality that includes memory limitation, computational cost, and low accuracy performance. Consequently, there is a need to reduce the dimension of datasets by choosing the most significant features that would represent the data efficiently with minimum volume. This study proposes an improved binary version of the equilibrium optimizer algorithm (IBEO) to mitigate features selection problem. Two main enhancements are added to the original equilibrium optimizer (EO) to strengthen its performance. Opposition based learning is the first advancement added to the initialization stage of EO to enhance the diversity of the population in the search space. Local search algorithm is the second advancement added to enhance the exploitation of EO. Wrapper approaches can offer premium solutions. Thus, we used k-nearest neighbour classifier and support vector machine classifiers as the most popular wrapper methods. Moreover, dealing with the problem of over-fitting is an essential task that urges on applying k-fold cross-validation to split each dataset into training and testing data. Comparative tests with different well-known algorithms such as grey wolf optimization, grasshopper optimization, particle swarm optimization, whale optimization, dragonfly, and improved salp swarm algorithms are considered. The proposed algorithm is applied to the most commonly datasets used in the field to validate the performance. Statistical analysis studies demonstrate the effectiveness of the IBEO.

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Section: Results and Analysismentioning

confidence: 99%

Section: ) Comparison Of Ibeo With Eo and Beomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

Elmanakhly¹,

Saleh²,

Rashed³

2021

IEEE Access

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“…BASO is a meta-heuristic algorithm in which functions well in a number of benchmark tests [30,31]. The BASO replicates molecular dynamics fundamental theory and atom movement principles such as potential function characteristics, contact force, and geometric constraint force [30,31]. Atoms are a term used to describe a population of solutions in BASO algorithm.…”

Section: Selected Features Using Binary Atom Search Optimization (Baso)mentioning

confidence: 99%

An efficient digital pulse processing approach for identification of BGO and LSO scintillator crystals

Tokhy

2023

J. Inst.

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Identification and discrimination between BGO and LSO scintillators is a fundamental target for handling parallax error within positron emission tomography (PET) applications by depth of interaction. An approach is built for discrimination and identification of BGO and LSO scintillator crystals. This approach is tested using a simulated BGO and LSO pulses. A Matlab Simulink model is implemented for simulation and creation of BGO and LSO scintillation pulses. The simulated pulses depend on 22Na radiation source. The suggested approach has two different algorithms. The first algorithm uses both 1D-Walsh ordered fast Walsh-Hadamard transform (1WFWHT) and fast Chebyshev transform (FCHT) for extracting the features of crystal pulses. The optimum features are selected from 1WFWHT and FCHT using one of three optimization techniques that are binary dragonfly optimization (BDA), binary atom search optimization (BASO) and binary Harris Hawk optimization (BHHO). These optimized features are trained and tested using one of three based classifiers. These classifiers are Naive Bayes classifier (NBC), hierarchical prototype-based (HP) classifier and adaptive neuro-fuzzy inference system (ANFIS) classification. The ANFIS classifier achieves the best accuracy with all optimum (BASO, BDF and BHH) FCHT features. However, the NB classifier introduces the highest accuracy with all optimum (BASO, BDF and BHH) FWHT 1D features. The second algorithm uses the conventional neural network (CNN) for extracting the pulse features. Then, the deep neural network (DNN) is applied for training and testing of the captured pulses. The suggested algorithms are verified and compared in respect of statistical measures. The compared results confirm that the best accuracy and identification rate is accomplished using DNN algorithm. Besides, the DNN has better results compared to conventional classification techniques with optimum feature selection techniques in respect of time consumption. The proposed approach aids in the realisation of overcoming parallax inaccuracy in PET.

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“…In [34], the authors implemented a general learning strategy in EOA, helping the search-agents to avoid the local optima and to improve the capability for discovering a promising area. Moreover, [35] integrated simulated annealing with the equilibrium algorithm to improve its local search. In our proposed algorithm, IEOA, EOBL is used at the initialization phase to improve the initial solutions created in the standard EOA.…”

Section: Related Workmentioning

confidence: 99%

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

et al. 2021

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The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.

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BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Cited by 11 publications

References 48 publications

An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis

An efficient digital pulse processing approach for identification of BGO and LSO scintillator crystals

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

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