Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Overview of Grey Wolf Optimizer Algorithm
Results and Discussion
Conclusion
References

Modified Grey Wolf Optimizer for Global Engineering Optimization

Profile image of Nitin MittalNitin Mittal
https://doi.org/10.1155/2016/7950348
visibility

…

description

17 pages

link

1 file

Abstract

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

Related papers

Dingo Optimizer: A Nature-Inspired Metaheuristic Approach for Engineering Problems
Amit Bairwa

Mathematical Problems in Engineering, 2021

Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo (Canis familiaris dingo). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.

View PDFchevron_right
Hybrid Genetic Grey Wolf Algorithm for Large Scale Global Optimization
Xuexian Li, Song Jiang

2019

Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, thewhole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population aremutated to prevent the algorithm fromfalling into local optimum.The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance ofHGGWA is compared against several state-of-the-art algorithms, CCPSO2,DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.

View PDFchevron_right
A Comprehensive Survey of Grey Wolf Optimizer Algorithm and its Application
Engineering Research Trends & Articles

International Journal of Advance Robotics & Expert Systems (JARES)

This study presents a comprehensive and through summary of the Grey Wolf Optimizer (GWO).The GWO algorithm is a newly-presented meta-heuristic, propelled from the social hunting behavior of grey wolves. The GWO has become a progressively critical device of Swarm Intelligence that has been used in nearly all zones of optimization, and engineering practice. Numerous issues from different regions have been effectively illuminated utilizing the GWO algorithm and its variants. In arrange to utilize the calculation to illuminate assorted issues, the original GWO algorithm required to be modified or hybridized. This study conducts an exhaustive review of this living and advancing area of Swarm Intelligence, so that to show that the GWO algorithm might be connected to each issue emerging in hone. However, it empowers novice researchers and algorithm developers to utilize this straightforward and however exceptionally effective algorithm for issue tackling. It frequently ensures that the gained results about will meet the expectation.

View PDFchevron_right
Niching Grey Wolf Optimizer for Multimodal Optimization Problems
Shuhaimi Mahadzir

Applied Sciences

Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark fu...

View PDFchevron_right
A New K means Grey Wolf Algorithm for Engineering Problems
Hardi Mohammed

2021

Purpose: This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach: The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO against the GWO. To evaluate the performance of the KMGWO, KMGWO was applied to solve CEC2019 benchmark test functions.

View PDFchevron_right
Meta-Heuristic Algorithm Inspired by Grey Wolves for Solving Function Optimization Problems
Alaa Tharwat

2016

In this paper, we suggest the use of Grey Wolf Optimization (GWO) algorithm to solve numerical optimization problems. GWO is compared with two well-known optimization algorithms namely, Bat Algorithm (BA) and Particle Swarm Optimization (PSO), to test the improvement in the accuracy of finding the near optimal solution and the reduction in the computational cost. Ten standard benchmark functions were applied to test the performance of the three optimization algorithms in terms of accuracy and computational cost. The experimental results proved that our proposed method achieved accuracy better than the other two algorithms and it reduced the computational cost and converged rapidly to the optimal solution.

View PDFchevron_right
Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons
Emmanuel Gbenga Dada

GAZI UNIVERSITY JOURNAL OF SCIENCE, 2021

Highlights • Comprehensive study on the recent trends and progress of GWO algorithm. • Elaborate study of several GWO variants available in literature. • Limitations of existing variants of GWO algorithms. • Applications and possible horizon of GWO algorithms. • Case studies of GWO algorithms in literature.

View PDFchevron_right
A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization
Prof. Dr. Hanaa Hachimi

The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer Algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

View PDFchevron_right
Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization
Amjad Mehmood

IEEE Access

Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to it's position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski's information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided.

View PDFchevron_right
An Improved Grey Wolf Optimization Algorithm with Variable Weights
Greenby Gao

Computational Intelligence and Neuroscience

With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.

View PDFchevron_right

References (53)

  1. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
  2. T. Back, F. Hoffmeister, and H. P. Schwefel, "A survey of evolution strategies, " in Proceedings of the 4th International Conference on Genetic Algorithms, San Diego, Calif, USA, July 1991.
  3. J. R. Koza, Genetic Programming: On the Programming of Computers by Natural Selection, MIT Press, Cambridge, UK, 1992.
  4. R. Storn and K. Price, "Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, " Journal of Global Optimization, vol. 11, no. 4, pp. 341- 359, 1997.
  5. D. Simon, "Biogeography-based optimization, " IEEE Transac- tions on Evolutionary Computation, vol. 12, no. 6, pp. 702-713, 2008.
  6. W. Gong, Z. Cai, C. X. Ling, and H. Li, "A real-coded biogeography-based optimization with mutation, " Applied Mathematics and Computation, vol. 216, no. 9, pp. 2749-2758, 2010.
  7. W. Gong, Z. Cai, and C. X. Ling, "DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization, " Soft Computing, vol. 15, no. 4, pp. 645- 665, 2011.
  8. H. Ma and D. Simon, "Blended biogeography-based optimiza- tion for constrained optimization, " Engineering Applications of Artificial Intelligence, vol. 24, no. 3, pp. 517-525, 2011.
  9. U. Singh and T. S. Kamal, "Design of non-uniform circular antenna arrays using biogeography-based optimisation, " IET Microwaves, Antennas and Propagation, vol. 5, no. 11, pp. 1365- 1370, 2011.
  10. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing, " Science, vol. 220, no. 4598, pp. 671-680, 1983.
  11. P. Moscato, "On evolution, search, optimization, genetic algo- rithms and martial arts: towards Memetic Algorithms, " Caltech Concurrent Computation Program Report 826, 1989.
  12. Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: harmony search, " Simulation, vol. 76, no. 2, pp. 60-68, 2001.
  13. K. S. Lee and Z. W. Geem, "A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, " Computer Methods in Applied Mechanics and Engineering, vol. 194, no. 36-38, pp. 3902-3933, 2005.
  14. M. Eusuff, K. Lansey, and F. Pasha, "Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization, " Engineering Optimization, vol. 38, no. 2, pp. 129-154, 2006.
  15. E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm, " Information Sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
  16. S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, "Multi-verse optimizer: a nature-inspired algorithm for global optimization, " Neural Computing & Applications, vol. 27, no. 2, pp. 495-513, 2016.
  17. A. Y. S. Lam and V. O. K. Li, "Chemical-reaction-inspired meta- heuristic for optimization, " IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 381-399, 2010.
  18. J. Kennedy and R. Eberhart, "Particle swarm optimization, " in Proceedings of the IEEE International Conference on Neural Network, vol. 4, pp. 1942-1948, Perth, Australia, December 1995.
  19. X.-S. Yang, "Firefly algorithm, stochastic test functions and design optimization, " International Journal of Bio-Inspired Com- putation, vol. 2, no. 2, pp. 78-84, 2010.
  20. X.-S. Yang and A. H. Gandomi, "Bat algorithm: a novel approach for global engineering optimization, " Engineering Computations, vol. 29, no. 5, pp. 464-483, 2012.
  21. M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimiza- tion by a colony of cooperating agents, " IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, no. 1, pp. 29-41, 1996.
  22. M. Dorigo and L. M. Gambardella, "Ant colony system: a coop- erative learning approach to the traveling salesman problem, " IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53-66, 1997.
  23. X.-S. Yang and S. Deb, "Engineering optimisation by cuckoo search, " International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330-343, 2010.
  24. D. Karaboga and B. Akay, "A comparative study of artificial Bee colony algorithm, " Applied Mathematics and Computation, vol. 214, no. 1, pp. 108-132, 2009.
  25. X. Li, Z. Shao, and J. Qian, "An optimizing method base on autonomous animates: fish swarm algorithm, " Systems Engineering-Theory & Practice, vol. 22, pp. 32-38, 2002.
  26. K. Krishnanand and D. Ghose, "Glowworm swarm optimi- sation: a new method for optimising multi-modal functions, " International Journal of Computational Intelligence Studies, vol. 1, no. 1, pp. 93-119, 2009.
  27. S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer, " Advances in Engineering Software, vol. 69, pp. 46-61, 2014.
  28. W.-T. Pan, "A new fruit fly optimization algorithm: taking the financial distress model as an example, " Knowledge-Based Systems, vol. 26, pp. 69-74, 2012.
  29. X.-B. Meng, X. Z. Gao, Y. Liu, and H. Zhang, "A novel bat algorithm with habitat selection and Doppler effect in echoes for optimization, " Expert Systems with Applications, vol. 42, no. 17-18, pp. 6350-6364, 2015.
  30. S. Mirjalili, "Dragonfly algorithm: a new meta-heuristic opti- mization technique for solving single-objective, discrete, and multi-objective problems, " Neural Computing & Applications, 2015.
  31. S. C. Chu and P. W. Tsai, "Computational intelligence based on the behaviour of cats, " International Journal of Innovative Computing Information and Control, vol. 3, pp. 163-173, 2007.
  32. R. Rajabioun, "Cuckoo optimization algorithm, " Applied Soft Computing Journal, vol. 11, no. 8, pp. 5508-5518, 2011.
  33. J. C. Bansal, H. Sharma, S. S. Jadon, and M. Clerc, "Spider Monkey Optimization algorithm for numerical optimization, " Memetic Computing, vol. 6, no. 1, pp. 31-47, 2014.
  34. D. Dasgupta, Artificial Immune Systems and Their Applications, Springer, 1999.
  35. S. Das, A. Biswas, S. Dasgupta, and A. Abraham, "Bacterial for- aging optimization algorithm: theoretical foundations, analysis, and applications, " Studies in Computational Intelligence, vol. 203, pp. 23-55, 2009.
  36. A. H. Gandomi and A. H. Alavi, "Krill herd: a new bio-inspired optimization algorithm, " Communications in Nonlinear Science and Numerical Simulation, vol. 17, no. 12, pp. 4831-4845, 2012.
  37. V. K. Kamboj, S. K. Bath, and J. S. Dhillon, "Solution of non-convex economic load dispatch problem using Grey Wolf Optimizer, " Neural Computing and Applications, 2015.
  38. E. Emary, H. M. Zawbaa, C. Grosan, and A. E. Hassenian, "Feature subset selection approach by gray-wolf optimization, " in Afro-European Conference for Industrial Advancement, vol. 334 of Advances in Intelligent Systems and Computing, Springer, 2015.
  39. S. Gholizadeh, "Optimal design of double layer grids consid- ering nonlinear behaviour by sequential grey wolf algorithm, " Journal of Optimization in Civil Engineering, vol. 5, no. 4, pp. 511-523, 2015.
  40. Y. Yusof and Z. Mustaffa, "Time series forecasting of energy commodity using grey wolf optimizer, " in Proceedings of the International Multi Conference of Engineers and Computer Scientists (IMECS '15), vol. 1, Hong Kong, March 2015.
  41. G. M. Komaki and V. Kayvanfar, "Grey wolf optimizer algo- rithm for the two-stage assembly flow shop scheduling problem with release time, " Journal of Computational Science, vol. 8, pp. 109-120, 2015.
  42. A. A. El-Fergany and H. M. Hasanien, "Single and multi- objective optimal power flow using grey wolf optimizer and differential evolution algorithms, " Electric Power Components and Systems, vol. 43, no. 13, pp. 1548-1559, 2015.
  43. K. Shankar and P. Eswaran, "A secure visual secret share (VSS) creation scheme in visual cryptography using elliptic curve cryptography with optimization technique, " Australian Journal of Basic & Applied Science, vol. 9, no. 36, pp. 150-163, 2015.
  44. E. Emary, H. M. Zawbaa, and A. E. Hassanien, "Binary grey wolf optimization approaches for feature selection, " Neurocom- puting, vol. 172, pp. 371-381, 2016.
  45. V. K. Kamboj, "A novel hybrid PSOGWOapproach for unit commitment problem, " Neural Computing and Applications, 2015.
  46. A. Zhu, C. Xu, Z. Li, J. Wu, and Z. Liu, "Hybridizing grey Wolf optimization with differential evolution for global optimization and test scheduling for 3D stacked SoC, " Journal of Systems Engineering and Electronics, vol. 26, no. 2, pp. 317-328, 2015.
  47. T.-S. Pan, T.-K. Dao, T.-T. Nguyen, and S.-C. Chu, "A communi- cation strategy for paralleling grey wolf optimizer, " Advances in Intelligent Systems and Computing, vol. 388, pp. 253-262, 2015.
  48. J. Jayapriya and M. Arock, "A parallel GWO technique for aligning multiple molecular sequences, " in Proceedings of the International Conference on Advances in Computing, Communi- cations and Informatics (ICACCI '15), pp. 210-215, IEEE, Kochi, India, August 2015.
  49. M. M. Afsar and M.-H. Tayarani-N, "Clustering in sensor networks: a literature survey, " Journal of Network and Computer Applications, vol. 46, pp. 198-226, 2014.
  50. W. B. Heinzelman, A. Chandrakasan, and H. Balakrish- nan, "Energy-efficient communication protocol for wireless microsensor networks, " in Proceedings of the 33rd Annual Hawaii International Conference on System Siences (HICSS '00), p. 223, IEEE, January 2000.
  51. N. Mittal and U. Singh, "Distance-based residual energy- efficient stable election protocol for WSNs, " Arabian Journal for Science and Engineering, vol. 40, no. 6, pp. 1637-1646, 2015.
  52. E. A. Khalil and B. A. Attea, "Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks, " Swarm and Evolutionary Computation, vol. 1, no. 4, pp. 195-203, 2011.
  53. B. A. Attea and E. A. Khalil, "A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks, " Applied Soft Computing, vol. 12, no. 7, pp. 1950-1957, 2012.

Related papers

Grey Wolf Optimizer
Andrew Lewis

Advances in Engineering Software, 2014

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these wellknown meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.

View PDFchevron_right
A Novel Hybrid Metaheuristic Based on Augmented Grey Wolf Optimizer and Cuckoo Search for Global Optimization
Satyam Sharma, Ridhi Kapoor

IEEE, 2021

Grey Wolf Optimizer (GWO) is an intelligent metaheuristic approach which imitates the leadership hierarchy and cooperative hunting behavior of a group of Grey wolves(wolfpack). An augmentation of GWO, named Augmented GWO (AGWO), was recently proposed which possesses greater exploration abilities. Nevertheless, in some cases, AGWO underperforms in the exploitation phase and stagnates at local optimum. The CS algorithm is a nature-inspired optimizing technique that mimics the unique nesting strategy of cuckoo birds and levy-flights. Both the algorithms possess powerful searching capabilities. In our research work, a novel hybrid metaheuristic, termed AGWOCS, is put forth, which combines the merits of both metaheuristics in order to attain global optimum effectively. The proposed algorithm amalgamates the exploring abilities of the AGWO with the exploiting abilities of the Cuckoo Search (CS). For the purpose of testing the proficiency of our proposed hybrid AGWOCS, twenty-three renowned benchmarking functions were used. It is compared with six other existing metaheuristics, including Standard GWO, Particle Swarm Optimization (PSO), Augmented-GWO (AGWO), Enhanced-GWO (EGWO), Hybrid GWO with CS (CS-GWO) and Hybrid PSO and GWO (GWOPSO). The simulation results indicate that AGWOCS surpasses other metaheuristics in terms of rapid convergence rates as well as avoiding local optimum stagnation.

View PDFchevron_right
Improved grey wolf algorithm for optimization problems
Indonesian Journal of Electrical Engineering and Computer Science

Indonesian Journal of Electrical Engineering and Computer Science, 2021

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.

View PDFchevron_right
Grey wolf optimizer: Overview, modifications and applications
Saman M. Almufti

Northern Negros State College of Science and Technology (NONESCOST), 2021

The complexity of real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Nondeterministic polynomial hard (NP-Hard) problems because of their ability to adjust to a variety of conditions. This paper describes Grey Wolf Optimizer (GWO) as a Swarm Based metaheuristic algorithm inspired by the leadership hierarchy and hunting behavior of the grey wolves for solving complex and real-world optimization problems. Since the appearance of GWO many modifications for improving the performance of the algorithm and have been applied to various applications in several fields. At the end of this paper, the improvements are listed.

View PDFchevron_right
Improved grey wolf optimisation algorithms
Greenby Gao

The Journal of Engineering

The grey wolf optimisation (GWO) algorithm has fewer numbers of variables and appears quite simple with outstanding capabilities in solving the problems, which are used to describe mathematically what human met in nature. However, it still has its capability to be improved in the convergence ratio, stability, and reduce the errors. And it is also easily trapped in local optimum and converged slowly approaching the end, which is just the same defect appearing in other metaheuristic algorithms such as the bat algorithm (BA), the particle swarm optimisation (PSO) algorithm, and the genetic algorithm (GA). Lots of improvements have been proposed before. In this paper, we propose an improved GWO algorithm inspired by the PSO algorithm to fasten the convergence ratio and reduce the errors. Empirical work and verifications are carried out; and results show its better performance than the standard GWO algorithm and other well-known meta-heuristic algorithms

View PDFchevron_right

Cited by

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection
davies segera

BioMed Research International

Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to existing machine learning approaches since the search space for identifying the most relevant feature subset is so diverse to be explored quickly while utilizing minimal computational resources. On the other hand, the second aspect poses a challenge of too few samples to learn from. Though many hybrid metaheuristic approaches (i.e., combining multiple search algorithms) have been proposed in the literature to address these challenges with very attractive performance compared to their counterpart standard standalone metaheuristics, more superior hybrid approaches can be achieved if the individual metaheuristics within the proposed hybrid algorithms are improved prior to the hybridization. M...

View PDFchevron_right
Optimal Selection of Gear Ratio for Hybrid Electric Vehicles Using Modern Meta-Heuristics Search Algorithm
sobhit saxena

E3S Web of Conferences

Gear Train Design problem is most important design problem for machine tools manufacturers. Recent work on gear train improvement has been bound towards multi-shaft gear trains of the speed-change kind, where major focus is to maximize the range of operating speeds and to minimize the number of gears and spindles. In the proposed research, a hybrid meta-heuristic search algorithm is presented to design and optimize multi-spindle gear trains problem. The objective of the research is to optimize gear trains on the basis of minimum overall centre distance, minimum overall size, minimum gear volume, or other desirable criteria, such as maximum contact or overlap ratios. The proposed hybrid meta-heuristic search algorithm is inspired by canis lupus family of grey wolves and exploitation capability of existing grey wolf optimizer is further enhanced by pattern search algorithm, which is a derivative-free, direct search optimization algorithm suitable for non-differential, discontinuous se...

View PDFchevron_right
A Grey Wolf-Based Method for Mammographic Mass Classification
Mohamed Meselhy Eltoukhy

Applied Sciences

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments hav...

View PDFchevron_right
An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem
Elias Olivares-Benitez

Mathematics

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited expl...

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

Algorithms

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.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved