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

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.

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

Mathematics ◽

10.3390/math8091457 ◽

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.

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

Applied Sciences ◽

10.3390/app10186343 ◽

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.

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An improved grey wolf optimizer for solving engineering problems

Expert Systems with Applications ◽

10.1016/j.eswa.2020.113917 ◽

2021 ◽

Vol 166 ◽

pp. 113917 ◽

Cited By ~ 4

Author(s):

Mohammad H. Nadimi-Shahraki ◽

Shokooh Taghian ◽

Seyedali Mirjalili

Keyword(s):

Grey Wolf Optimizer ◽

Grey Wolf ◽

Engineering Problems ◽

Improved Grey Wolf Optimizer

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Collaborative Strategy for Grey Wolf Optimization Algorithm

Modern Applied Science ◽

10.5539/mas.v12n7p73 ◽

2018 ◽

Vol 12 (7) ◽

pp. 73 ◽

Cited By ~ 1

Author(s):

Esra F. Alzaghoul ◽

Sandi N. Fakhouri

Keyword(s):

Optimization Algorithm ◽

State Of The Art ◽

Particle Swarm ◽

Search Space ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Particle Swarm Optimizer ◽

Minimal Points ◽

Search Feature ◽

Sine Cosine Algorithm

Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.

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Developing Software of Global Optimization Method Based on Grey Wolf Optimizer

Modelling and Data Analysis ◽

10.17759/mda.2021110204 ◽

2021 ◽

Vol 11 (2) ◽

pp. 59-73

Author(s):

A.V. Panteleev ◽

I.A. Belyakov

Keyword(s):

Optimization Method ◽

Step Function ◽

Algorithm Analysis ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Test Functions ◽

Global Extremum ◽

The Cost ◽

Bioinspired Algorithms ◽

Selection Of

This article discusses the development of software that allows to simulate the algorithm of the “Grey Wolf Optimizer” method. This algorithm belongs to the class of metaheuristic algorithms that allow finding a global extremum on a set of admissible solutions. This algorithm is being the most efficiently used in a situation where the cost function is specified in the form of a black box. The algorithm belongs to both bioinspired algorithms and to the class of algorithms of Particle Swarm Optimization. To analyze the efficiency of the algorithm, software was created that allows to vary the parameters of the method. The article contains examples of the program’s work on various test functions. The purpose of the program is to collect and analyze statistical results, making possible to evaluate the final result. The program provides to build graphs that make it possible to make a more thorough assessment of the results obtained. The program has a step-by-step function that allows one to analyze the specifics and features of the algorithm. Analysis of statistical data provides more detailed selection of the parameters of the algorithm.

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An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

Electronics ◽

10.3390/electronics10243178 ◽

2021 ◽

Vol 10 (24) ◽

pp. 3178

Author(s):

Pu Lan ◽

Kewen Xia ◽

Yongke Pan ◽

Shurui Fan

Keyword(s):

Chaos Theory ◽

Prediction Accuracy ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Practical Applications ◽

Marine Predator ◽

Search Speed ◽

Improved Grey Wolf Optimizer ◽

Better Than

In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.

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Forecasting Energy Demand of India Using Integrated Grey Wolf Optimizer

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9239 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3605-3612

Author(s):

Shailender Kumar ◽

Kamran Sayeed ◽

Anubhav Chhikara ◽

Durin Dai

Keyword(s):

Energy Demand ◽

Slow Growth ◽

Primary Energy ◽

Grey Wolf Optimizer ◽

Economic Factors ◽

Coal Production ◽

Grey Wolf ◽

Energy Demands ◽

Form Model ◽

Better Than

In this paper we provide a new methodology for estimating the future primary energy demands of India. Firstly, we propose a new algorithm known as Integrated Grey wolf Optimizer. This new algorithm is an improvement over Grey wolf optimizer to deal with multimodal functions. Economic factors such as GDP (Gross Domestic Product), Population, Coal production and Petroleum production are used as mathematical parameters for our objective function. The coefficients of this two-form model (i.e., Linear and Quadratic) are optimized using the new Integrated Grey wolf optimizer. The highlight of this extract is the new Integrated version of grey wolf optimizer, which improves the exploration capability of the algorithm to deal with local minima stagnation. The results of this modified version are better than traditional Grey wolf optimizer and provides better accuracy and less errors. The last 14 years of historical information of India are used as datasets for the respective parameters. Coefficients obtained after the optimization are used for forecasting in three different cases which are Rapid (7.5% rise in GDP), Moderate (6.5%) and (5.5%) Slow growth of country.

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Grey Wolf Algorithm for Requirements Prioritization

Modern Applied Science ◽

10.5539/mas.v12n2p54 ◽

2018 ◽

Vol 12 (2) ◽

pp. 54 ◽

Cited By ~ 7

Author(s):

Raja Masadeh ◽

Abdullah Alzaqebah ◽

Amjad Hudaib

Keyword(s):

Requirement Engineering ◽

Software Project ◽

Grey Wolf ◽

Hunting Behavior ◽

Important Approach ◽

Dataset Size ◽

Average Running Time ◽

Hierarchy Process ◽

Requirement Prioritization ◽

Better Than

Requirement prioritization is one of the most important approach in the process of requirement engineering due to use it in order to prioritize the execution sort of requirements with taking into account the viewpoints of stakeholders. Thus, in this study, grey wolf optimization (GWO) algorithm is applied in order to prioritize the requirements of a software project. GWO imitates the hunting behavior of grey wolves in nature. Which distinct from others that it has dominant leadership hierarchy which contains four main types; alpha, beta delta and omega wolves. In this paper, a proposed algorithm is presented to prioritize the requirements into ordered list. Furthermore, it is compared and evaluated with analytical hierarchy process (AHP) technique in terms of average running time and dataset size. The findings display that the RP-GWO performs better than AHP mechanism by approximately (30%).

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Niching Grey Wolf Optimizer for Multimodal Optimization Problems

Applied Sciences ◽

10.3390/app11114795 ◽

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.

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Identification of Parameters of Richards Equation Using Modified Hybrid Grey Wolf Optimizer-Genetic Algorithm (HmGWOGA)

European Journal of Pure and Applied Mathematics ◽

10.29020/nybg.ejpam.v12i4.3564 ◽

2019 ◽

Vol 12 (4) ◽

pp. 1567-1583

Author(s):

Wenddabo Olivier Sawadogo ◽

Pengdwende Ousseni Fabrice Ouedraogo ◽

Ousseni So ◽

Genevieve Barro ◽

Blaise Some

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Meshless Method ◽

Direct Problem ◽

Pressure Head ◽

Optimization Method ◽

Richards Equation ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Better Than

In this paper, it is a question of identification of the parameters in the equation ofRichards modelling the flow in unsaturated porous medium. The mixed formulation pressure head-moisture content has been used. The direct problem was solved using Multiquadratic Radial Basis Function ( RBF-MQ ) method which is a meshless method. The Newton-Raphson’s method was used to linearize the equation. The function cost used is built by using the infiltration. The optimization method used is a meta-heuristic called Modified hybrid Grey Wolf Optimizer -Genetic Algorithm (HmGWOGA). A test on experimental data has been carried. We compared the results with genetic algorithms. The results showed that this new method was better than genetic algorithms.

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