An efficient binary Gradient-based optimizer for feature selection

Jiang, Yugui; Luo, Qifang; Wei, Yuanfei; Abualigah, Laith; Zhou, Yongquan

doi:10.3934/mbe.2021192

Cited by 48 publications

(15 citation statements)

References 84 publications

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“…Traditional optimization methods have several drawbacks when solving complex and complicated problems that require considerable time and cost optimization. Metaheuristic algorithms have been proven capable of handling a variety of continuous and discrete optimization problems [46] in a wide range of applications including engineering [47][48][49], industry [50,51], image processing and segmentation [52][53][54], scheduling [55,56], photovoltaic modeling [57,58], optimal power flow [59,60], power and energy management [61,62], planning and routing problems [63][64][65], intrusion detection [66,67], feature selection [68][69][70][71][72], spam detection [73,74], medical diagnosis [75][76][77], quality monitoring [78], community detection [79], and global optimization [80][81][82]. In the following, some representative metaheuristic algorithms from the swarm intelligence category used in our experiments are described.…”

Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

et al. 2021

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“…In the second part of experiment, the performance of the proposed model is evaluated with the different recent binary versions such as binary particle swarm optimization (Khanesar et al, 2007), binary genetic algorithm (Rashedi et al, 2009), binary grey wolf optimization (Emary et al, 2016), binary Harris hawk optimization (Too et al, 2019a), binary manta ray forging optimization (Ghosh, Guha, et al, 2020) binary salp swarm algorithm(Faris et al, 2018), binary atom search optimization (Abdullah & Rahim Abdullah, 2020), binary marine predator algorithm (Salama et al, 2021), binary moth‐flame optimization (Nadimi‐Shahraki et al, 2021), binary gradient‐based optimizer (Jiang et al, n.d.), binary social mimic optimization (Ghosh, Singh, et al, 2020), binary artificial algae algorithm (Turkoglu et al, 2022), binary political optimization (Korbaa & Korbaa, n.d.), binary hunger games search optimization (Devi et al, 2021) and binary butterfly optimization algorithm (Arora & Anand, 2018) which are widely applied to solve the FS problems in the literature. Table 6 shows the parameter settings of state‐of‐the‐art binary MH algorithms.…”

Section: Resultsmentioning

confidence: 99%

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

2022

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The African vulture optimization algorithm (AVOA) is a recently developed metaheuristic algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA is developed to address the continuous optimization problem. However, AVOA is unable to solve the discrete search space, this inspires us to develop the binary AVOA for feature selection problems in classification tasks. The suggested BAVOA incorporates an eight-transfer function (S-shaped and V-shaped) for transforming a continuous variable to a binary one. Using 14 benchmark data sets, the proposed technique is compared against 15 conventional binary metaheuristics algorithms in terms of classification accuracy, fitness function, number of selected features and converging ability. Furthermore, the results are statistically analysed using Wilcoxon test. The comparative findings of S-Shaped and V-shaped transfer functions indicate the superior performance of BAVOA methods, particularly S2-BAVOA, in contrast to other transfer function. Based on results, it turns out that the suggested technique converges to the global minimum in several iterations based on the selection of optimal characteristics, fitness values and higher classification accuracy as compared to the classical binary metaheuristic algorithms.

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“…But, in dealing with the problems of binary optimization, GBO operators must be reformulated to be applicable to such types of optimization problems. A binary type of GBO (BGBO) has been introduced by Jiang et al [36] for feature selection in unsupervised learning. BGBO and the sum of squared errors Utilizing OBGBO to find the optimal parameters in solar photovoltaic models…”

Section: Binarymentioning

confidence: 99%

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud

Shehab

Al-Mimi

et al. 2022

Arch Computat Methods Eng

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This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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An efficient binary Gradient-based optimizer for feature selection

Cited by 48 publications

References 84 publications

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

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