A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Mohammadzadeh, Hekmat; Gharehchopogh, Farhad Soleimanian

doi:10.1111/coin.12397

Cited by 88 publications

(24 citation statements)

References 50 publications

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“…WOA consists of three phases: encircling the prey, bubble-net attacking, and searching for the prey. WOA has been used to solve a wide range of optimization problems in different applications including feature selection [28], software defect prediction [29], clustering [30,31], classification [32,33], disease diagnosis [34], image segmentation [35,36], scheduling [37], forecasting [38,39], parameter estimation [40], global optimization [41], and photovoltaic energy generation systems [42,43]. Even though WOA is employed to tackle a wide variety of optimization problems, it still has flaws such as premature convergence, the imbalance between exploration and exploitation, and local optima stagnation [44,45].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…FPA was first used to solve the mathematical test functions, and then it was utilized to find the best reservoir operations for downstream water supply and hydropower generation. The authors observed that FPA received widespread attention and been addressed in various optimization problems including power system [30] for economic and dispatch problems, dispatching distribution network problem [31], feature selection problem [32] in data mining, and so on. However, FPA metaheuristic has been adopted around combinatorial tway testing for test suite generation [33], [34].…”

Section: Evolution-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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“…Feature selection can address this problem, through which a subset of the relevant and effective features must be found. Feature selection is used in a variety of real-world applications such as disease diagnosis [4,5], email spam detection [6], text clustering [7,8], and human activity recognition [9].…”

Section: Introductionmentioning

confidence: 99%

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

et al. 2021

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Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

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A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Cited by 88 publications

References 50 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

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

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