Feature Subset Selection Approach by Gray-Wolf Optimization

Emary, Eid; Zawbaa, Hossam M.; Groşan, Crina; Hassenian, Abul Ella

doi:10.1007/978-3-319-13572-4_1

Cited by 141 publications

(71 citation statements)

References 18 publications

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“…Grey Wolf Optimizer is recently developed metaheuristics inspired from the hunting mechanism and leadership hierarchy of grey wolves in nature and has been successfully applied for solving optimizing key values in the cryptography algorithms [15], feature subset selection [16], time forecasting [17], optimal power flow problem [18], economic dispatch problems [19], flow shop scheduling problem [20], and optimal design of double layer grids [21]. Several algorithms have also been developed to improve the convergence performance of Grey Wolf Optimizer that includes parallelized GWO [22,23], binary GWO [24], integration of DE with GWO [25], hybrid GWO with Genetic Algorithm (GA) [26], hybrid DE with GWO [27], and hybrid Grey Wolf Optimizer using Elite Opposition Based Learning Strategy and Simplex Method [28].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Singh

2017

Journal of Applied Mathematics

214

143

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A newly hybrid nature inspired algorithm called HPSOGWO is presented with the combination of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). The main idea is to improve the ability of exploitation in Particle Swarm Optimization with the ability of exploration in Grey Wolf Optimizer to produce both variants' strength. Some unimodal, multimodal, and fixeddimension multimodal test functions are used to check the solution quality and performance of HPSOGWO variant. The numerical and statistical solutions show that the hybrid variant outperforms significantly the PSO and GWO variants in terms of solution quality, solution stability, convergence speed, and ability to find the global optimum.

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Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Singh

2017

Journal of Applied Mathematics

214

143

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“…Kamboj et al [77] successfully applied GWO in solving economic dispatch problems. Emary et al [78] dealt with feature subset selection problems using GWO. Gholizadeh [79] utilized GWO to find the best design for nonlinear optimization problem of double layer grids.…”

Section: Grey Wolf Optimizer Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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“…The GWO is utilized to solve many optimization problems in different fields and successfully provides highly competitive results [49]- [52].…”

Section: Grey Wolf Search Algorithmmentioning

confidence: 99%

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Sheta¹,

Abdel-Raouf²

2016

ijacsa

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Abstract-In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM's parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.

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Feature Subset Selection Approach by Gray-Wolf Optimization

Cited by 141 publications

References 18 publications

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

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