scholarly journals Modified Grey Wolf Optimizer for Global Engineering Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Nitin Mittal ◽  
Urvinder Singh ◽  
Balwinder Singh Sohi
Keyword(s):  
Parameter Tuning ◽  
Global Optimum ◽  
Benchmark Problems ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Hunting Behavior ◽  
Clustering Problem ◽  
Global Engineering ◽  
The Impact

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.

scholarly journals A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

2021 ◽  
Author(s):  
Prathap Siddavaatam
Keyword(s):  
Network Architecture ◽  
Design Space Exploration ◽  
Synthesis Process ◽  
Benchmark Problems ◽  
The Internet ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Exploration Process ◽  
Multi Objective ◽  
The Impact

Today, Internet of Things (IoT) is a major paradigm shift that will mark an epoch in communication technology such that every physical object can be connected to the Internet. With the advent of 5G communications, IoT is in urgent need of optimized architectures that can efficiently support wide ranging heterogeneous multi-objective requirements of communication, hardware and security aspects. The optimization challenges are rooted in the technology and how the information is acquired and manipulated by this technology. My research in this thesis provides a description of compelling challenges faced by IoT and how to mitigate these challenges by designing resource-aware communication protocols, resource- constrained device hardware with low computing power and low-powered computational security enhancements. This thesis lays the foundation for optimizing these challenging IoT paradigms by introducing a novel Delta-Diagram based synthesizing model. The Delta- Diagram provides a road-map linking the behavioral and structural domains of a given IoT paradigm to generate respective optimizer domain parameters, which can be utilized by any optimizer framework. The fundamental part of the communication synthesizer is a mathematical model, developed to obtain the best possible routing paths and communication parameters among things. The ultimate aim of the entire synthesis process is to devise a design automation tool for IoT, which exploits the interrelations between different layer functionalities. This thesis also proposes a novel cross-layer Grey wolf optimizer for IoT, which outperforms some of the contemporary optimizer algorithms such as Particle Swarm, Genetic Algorithm, Differential Evolution optimizers in solving unimodal, multi-modal and composition benchmark problems. The purpose of this optimizer is to accurately capture both the high heterogeneity of the IoT and the impact of the Internet as part of delta diagram synthesis enabled network architecture. In addition, the Grey wolf optimizer for IoT plays a crucial role in design exploration of system on chip architecture for IoT device hardware. The results generated by the optimizer yielded the most optimum feasible solutions in the design space exploration process of the IoT.

scholarly journals A Delta diagram synthesis for IoT optimization with grey wolf driven multi-objective automation

2021 ◽  
Author(s):  
Prathap Siddavaatam
Keyword(s):  
Network Architecture ◽  
Design Space Exploration ◽  
Synthesis Process ◽  
Benchmark Problems ◽  
The Internet ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Exploration Process ◽  
Multi Objective ◽  
The Impact

Today, Internet of Things (IoT) is a major paradigm shift that will mark an epoch in communication technology such that every physical object can be connected to the Internet. With the advent of 5G communications, IoT is in urgent need of optimized architectures that can efficiently support wide ranging heterogeneous multi-objective requirements of communication, hardware and security aspects. The optimization challenges are rooted in the technology and how the information is acquired and manipulated by this technology. My research in this thesis provides a description of compelling challenges faced by IoT and how to mitigate these challenges by designing resource-aware communication protocols, resource- constrained device hardware with low computing power and low-powered computational security enhancements. This thesis lays the foundation for optimizing these challenging IoT paradigms by introducing a novel Delta-Diagram based synthesizing model. The Delta- Diagram provides a road-map linking the behavioral and structural domains of a given IoT paradigm to generate respective optimizer domain parameters, which can be utilized by any optimizer framework. The fundamental part of the communication synthesizer is a mathematical model, developed to obtain the best possible routing paths and communication parameters among things. The ultimate aim of the entire synthesis process is to devise a design automation tool for IoT, which exploits the interrelations between different layer functionalities. This thesis also proposes a novel cross-layer Grey wolf optimizer for IoT, which outperforms some of the contemporary optimizer algorithms such as Particle Swarm, Genetic Algorithm, Differential Evolution optimizers in solving unimodal, multi-modal and composition benchmark problems. The purpose of this optimizer is to accurately capture both the high heterogeneity of the IoT and the impact of the Internet as part of delta diagram synthesis enabled network architecture. In addition, the Grey wolf optimizer for IoT plays a crucial role in design exploration of system on chip architecture for IoT device hardware. The results generated by the optimizer yielded the most optimum feasible solutions in the design space exploration process of the IoT.

scholarly journals Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3500 ◽  
Author(s):  
Bishwajit Dey ◽  
Fausto Pedro García Márquez ◽  
Sourav Kr. Basak
Keyword(s):  
Low Voltage ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Energy Resources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Renewable Energy Resources ◽  
Generation Cost ◽  
Sine Cosine Algorithm ◽  
The Impact

Optimal scheduling of distributed energy resources (DERs) of a low-voltage utility-connected microgrid system is studied in this paper. DERs include both dispatchable fossil-fueled generators and non-dispatchable renewable energy resources. Various real constraints associated with adjustable loads, charging/discharging limitations of battery, and the start-up/shut-down time of the dispatchable DERs are considered during the scheduling process. Adjustable loads are assumed to the residential loads which either operates throughout the day or for a particular period during the day. The impact of these loads on the generation cost of the microgrid system is studied. A novel hybrid approach considers the grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) to minimize the overall generation cost of the microgrid system. It has been found that the generation costs rise 50% when the residential loads were included along with the fixed loads. Active participation of the utility incurred 9–17% savings in the system generation cost compared to the cases when the microgrid was operating in islanded mode. Finally, statistical analysis has been employed to validate the proposed hybrid Modified Grey Wolf Optimization-Sine Cosine Algorithm-Crow Search Algorithm (MGWOSCACSA) over other algorithms used.

scholarly journals An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1457
Author(s):  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Alma Rodríguez ◽  
Abraham Mendoza ◽  
Elias Olivares-Benitez
Keyword(s):  
Supplier Selection ◽  
Search Strategy ◽  
Optimization Problems ◽  
Displacement Vector ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Order Quantity ◽  
Hunting Behavior

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 exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

scholarly journals Travelling Salesman Problem Solution Based-on Grey Wolf Algorithm over Hypercube Interconnection Network

2018 ◽  
Vol 12 (8) ◽  
pp. 142 ◽  
Author(s):  
Ameen Shaheen ◽  
Azzam Sleit ◽  
Saleh Al-Sharaeh
Keyword(s):  
Optimization Problems ◽  
Interconnection Network ◽  
Travelling Salesman Problem ◽  
Benchmark Problems ◽  
Grey Wolf Optimizer ◽  
Gray Wolf ◽  
Problem Solution ◽  
Grey Wolf ◽  
Travelling Salesman ◽  
Complete Problems

Travelling Salesman Problem (TSP) is one of the most popular NP-complete problems for the researches in the field of computer science which focused on optimization. TSP goal is to find the minimum path between cities with a condition of each city must to visit exactly once by the salesman. Grey Wolf Optimizer (GWO) is a new swarm intelligent optimization mechanism where it success in solving many optimization problems. In this paper, a parallel version of GWO for solving the TSP problem on a Hypercube Interconnection Network is presented. The algorithm has been compared to the alternative algorithms. Algorithms have been evaluated analytically and by simulations in terms of execution time, optimal cost, parallel runtime, speedup and efficiency. The algorithms are tested on a number of benchmark problems and found parallel Gray wolf algorithm is promising in terms of speed-up, efficiency and quality of solution in comparison with the alternative algorithms.   

scholarly journals An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haiqiang Liu ◽  
Gang Hua ◽  
Hongsheng Yin ◽  
Yonggang Xu
Keyword(s):  
Compressed Sensing ◽  
Research Area ◽  
Global Optimum ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Distributed Compressed Sensing ◽  
Mixed Support ◽  
Important Research Area ◽  
Thresholding Algorithm ◽  
Optimum Solution

Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.

scholarly journals Niching Grey Wolf Optimizer for Multimodal Optimization Problems

2021 ◽  
Vol 11 (11) ◽  
pp. 4795
Author(s):  
Rasel Ahmed ◽  
Amril Nazir ◽  
Shuhaimi Mahadzir ◽  
Mohammad Shorfuzzaman ◽  
Jahedul Islam
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Metaheuristic Algorithms ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Test Functions ◽  
Search Technique ◽  
Industrial Community ◽  
Global Optima

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 functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly.

An Improved Grey Wolf Optimizer (iGWO) for Global Engineering Optimization

2020 ◽  
Vol 17 (1) ◽  
pp. 359-362
Author(s):  
Bhavesh N. Gohil ◽  
Dhiren R. Patel
Keyword(s):  
Optimization Problems ◽  
Grey Wolf Optimizer ◽  
Control Parameters ◽  
Grey Wolf ◽  
Key Factors ◽  
Global Engineering ◽  
Efficiency And Effectiveness ◽  
The Stability ◽  
Benchmark Test Functions ◽  
Specific Control

Metaheuristic algorithms are more popular due to their ability to solve generic problems. Selection and tuning of their algorithm specific control parameters are the key factors for their success. Grey Wolf Optimizer, an algorithm having less dependency on their control parameters and influenced by the hunting approach and social dominant hierarchy of grey wolves is considered here to solve global optimization problems. An improved GWO is proposed to achieve a perfect ratio of exploitation and exploration phases and gives more weight to the most suitable wolves to find the new position of grey wolves during the stated iterations. Simulation results of standard benchmark test functions convey the stability, efficiency, and effectiveness of iGWO compared with GWO, and other standard algorithms.

scholarly journals A modified grey wolf optimizer for improving wind plant energy production

2020 ◽  
Vol 18 (3) ◽  
pp. 1123
Author(s):  
Mohd Zaidi Mohd Tumari ◽  
Mohd Helmi Suid ◽  
Mohd Ashraf Ahmad
Keyword(s):  
Total Energy ◽  
Energy Production ◽  
Wind Farm ◽  
Control Policy ◽  
Convergence Condition ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Turbulence Effect ◽  
Large Wind

<span>The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods.</span>

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