A New Bio-Inspired Algorithm Based on the Hunting Behavior of Cheetah

2020 ◽  
Vol 11 (4) ◽  
pp. 13-30
Author(s):  
D. Saravanan ◽  
P. Victer Paul ◽  
S. Janakiraman ◽  
Ankur Dumka ◽  
L. Jayakumar
Keyword(s):  
Optimal Solution ◽  
Computational Time ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hunting Behavior ◽  
Slow Convergence ◽  
Efficient Communication ◽  
Group Hunting ◽  
Searching Ability ◽  
Real World Problems

Soft computing is recognized as the fusion of methodologies mainly designed to model and formulate solutions to real-world problems that are too difficult to model mathematically. The grey wolf optimizer (GWO) algorithm is the recently proposed bio-inspired optimization algorithm that is mainly based on their foraging and hunting behavior. This GWO is proved as the recent and best in solving complex problems, but they too face some drawbacks of low solving precision, slow convergence, and bad local searching ability. In order to overcome the shortcomings of the existing algorithms, this paper is intended to propose a novel algorithm based on the foraging behavior of the cheetah. The cheetah is well known for their leadership hierarchy, decision making, and efficient communication capabilities between their teammates during group hunting. The famous benchmark functions such as unimodal and multimodal functions are being chosen as the testbed, and the experiments are performed on them. The proposed scheme outperforms in terms of computational time and optimal solution.

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 An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6343
Author(s):  
Yuanyuan Liu ◽  
Jiahui Sun ◽  
Haiye Yu ◽  
Yueyong Wang ◽  
Xiaokang Zhou
Keyword(s):  
Differential Evolution ◽  
Image Recognition ◽  
Optimal Solution ◽  
Tsallis Entropy ◽  
Grey Wolf Optimizer ◽  
Initial Population ◽  
Grey Wolf ◽  
Particle Swarm Optimizer ◽  
De Algorithm ◽  
Local Optimal Solution

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

Software Release Planning Using Grey Wolf Optimizer

2020 ◽  
pp. 1-44
Author(s):  
Vibha Verma ◽  
Neha Neha ◽  
Anu G. Aggarwal
Keyword(s):  
Optimization Problems ◽  
Cost Model ◽  
Optimal Solution ◽  
Reduction Factor ◽  
Grey Wolf Optimizer ◽  
Reliability Growth ◽  
Grey Wolf ◽  
Software Cost ◽  
Release Planning ◽  
Software Release

This chapter presents the application of grey wolf optimizer in software release planning considering warranty based on the proposed mathematical model that measures reliability growth of software systems. Hence, optimal release and warranty time is determined while minimizing the overall software development cost. The software cost model is based on failure phenomenon modelled by incorporating fault removal efficiency, fault reduction factor, and error generation. The model has been validated on the fault dataset of ERP systems. Sensitivity analysis has been carried out to study the discrete changes in the cost parameter due to changes in optimal solution. The work significantly contributes to the literature by fulfilling gaps of reliability growth models, release problems considering warranty, and efficient ways for solving optimization problems. Further, the grey wolf optimizer result has been compared with genetic algorithm and particle swarm optimization techniques.

scholarly journals Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Linguo Li ◽  
Lijuan Sun ◽  
Jian Guo ◽  
Jin Qi ◽  
Bin Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Optimal Solution ◽  
Weight Coefficient ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Original Algorithm ◽  
Image Thresholding ◽  
Bee Colony ◽  
Optimal Thresholds ◽  
Objective Function Values

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability.

scholarly journals Optimal Power Flow Solutions Incorporating Stochastic Solar Power withthe Application Grey Wolf Optimize

2021 ◽  
Vol 03 (01) ◽  
pp. 74-84
Author(s):  
Nadir Taleb ◽  
◽  
Bachir Bentouati ◽  
Saliha Chettih ◽  
◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Solution ◽  
Electrical Network ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Renewable Energy Resources ◽  
Network Constraints ◽  
Optimal Power

The present paper aims to validate an electrical network study in consisting of conventional fossil fuel generators with the integration of intermittent generation technologies based on renewable energy resources like wind power or solar photovoltaic (PV) are the stochastic power output. By using an optimal power flow (OPF) problem different frameworks are developed for solving that represent various operating requirements, such as minimization of production fuel cost, and preserving generation emission at the lowest levels... etc. The OPF analysis aims to find the optimal solution and is very important for power system operation with satisfying operational constraints, planning and energy management. However, the intermittent combination of solar exacerbates the complexity of the problem. Within the framework of these criteria, this paper is an overview of the application Grey Wolf Optimizer (GWO) algorithm which solves the OPF problem with renewable energy. The algorithm thus combined and constructed gives optimum results satisfying all network constraints. Give an explanation for findings are based thus need to be with the optimum to effectuate of network constraints.

Software Release Planning Using Grey Wolf Optimizer

2022 ◽  
pp. 508-541
Author(s):  
Vibha Verma ◽  
Neha Neha ◽  
Anu G. Aggarwal
Keyword(s):  
Optimization Problems ◽  
Cost Model ◽  
Optimal Solution ◽  
Reduction Factor ◽  
Grey Wolf Optimizer ◽  
Reliability Growth ◽  
Grey Wolf ◽  
Software Cost ◽  
Release Planning ◽  
Software Release

This chapter presents the application of grey wolf optimizer in software release planning considering warranty based on the proposed mathematical model that measures reliability growth of software systems. Hence, optimal release and warranty time is determined while minimizing the overall software development cost. The software cost model is based on failure phenomenon modelled by incorporating fault removal efficiency, fault reduction factor, and error generation. The model has been validated on the fault dataset of ERP systems. Sensitivity analysis has been carried out to study the discrete changes in the cost parameter due to changes in optimal solution. The work significantly contributes to the literature by fulfilling gaps of reliability growth models, release problems considering warranty, and efficient ways for solving optimization problems. Further, the grey wolf optimizer result has been compared with genetic algorithm and particle swarm optimization techniques.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Amit Kumar Bairwa ◽  
Sandeep Joshi ◽  
Dilbag Singh
Keyword(s):  
Canis Familiaris ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Test Functions ◽  
Particle Swarm Optimizer ◽  
Hunting Behavior ◽  
Engineering Problems ◽  
Nature Inspired Algorithms ◽  
The Comparative Study ◽  
Better Than

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.

scholarly journals The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer

2022 ◽  
Vol 12 (1) ◽  
pp. 122
Author(s):  
Ali Iqbal Abbas ◽  
Afaneen Anwer
Keyword(s):  
Power Systems ◽  
Production Cost ◽  
Unit Commitment ◽  
Optimal Solution ◽  
Suggested Method ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Quadratic Programming Method

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.

scholarly journals Optimal control of fractional integro-differential systems based on a spectral method and grey wolf optimizer

2020 ◽  
Vol 10 (1) ◽  
pp. 55-65 ◽  
Author(s):  
Raheleh Khanduzi ◽  
Asyieh Ebrahimzadeh ◽  
Samaneh Panjeh Ali Beik
Keyword(s):  
Optimal Control ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Optimal Value ◽  
Local Methods ◽  
The Cost ◽  
Solution Accuracy

This paper elaborated an effective and robust metaheuristic algorithm with acceptable performance based on solution accuracy. The algorithm applied in solution of the optimal control of fractional Volterra integro-differential (FVID) equation which be substituted by nonlinear programming (NLP). Subsequently the FIVD convert the problem to a NLP by using spectral collocation techniques and thereafter we execute the grey wolf optimizer (GWO) to improve the speed and accuracy and find the solutions of the optimal control and state as well as the optimal value of the cost function. It is mentioned that the utilization of the GWO is simple, due to the fact that the GWO is global search algorithm, the method can be applied to find optimal solution of the NLP. The efficiency of the proposed scheme is shown by the results obtained in comparison with the local methods. Further, some illustrative examples introduced with their approximate solutions and the results of the present approach compared with those achieved using other methods.

Sign in / Sign up

Export Citation Format

Share Document