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.

scholarly journals A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

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 TV White Space Network Power Allocation Using Hybrid Grey Wolf Optimizer with Firefly Algorithm and Particle Swarm Optimization

2021 ◽  
Vol 20 ◽  
pp. 66-75
Author(s):  
Kennedy Ronoh ◽  
George Kamucha
Keyword(s):  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Firefly Algorithm ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Secondary Users ◽  
Swarm Optimization ◽  
Network Power

TV white spaces (TVWS) can be utilized by Secondary Users (SUs) equipped with cognitive radio functionality on the condition that they do not cause harmful interference to Primary Users (PUs). Optimization of power allocation is necessary when there is a high density of secondary users in a network in order to reduce the level of interference among SUs and to protect PUs against harmful interference. Grey Wolf Optimizer (GWO) is relatively recent population based metaheuristic algorithm that has shown superior performance compared to other population based metaheuristic algorithms. Recent trend has been to hybridize population based metaheuristic algorithms in order to avoid the problem of getting trapped in a local optimum. This paper presents the design and analysis of performance of a hybrid grey wolf optimizer and Firefly Algorithm (FA) with Particle Swarm Optimization operators for optimization of power allocation in TVWS network power allocation as a continuous optimization problem. Matlab was used for simulation. The hybrid of GWO, FA and PSO (HFAGWOPSO) reduces sum power by 81.42% compared to GWO and improves sum throughput by 16.41% when compared to GWO. Simulation results also show that the algorithm has better convergence rate.

scholarly journals A Box-Girder Design Using Metaheuristic Algorithms and Mathematical Test Functions for Comparison

2021 ◽  
Vol 2 (4) ◽  
pp. 891-910
Author(s):  
Károly Jármai ◽  
Csaba Barcsák ◽  
Gábor Zoltán Marcsák
Keyword(s):  
Optimization Problems ◽  
Real Life ◽  
Optimization Techniques ◽  
Metaheuristic Algorithms ◽  
Test Functions ◽  
Function Generator ◽  
Box Girder ◽  
Test Function ◽  
Advantages And Disadvantages ◽  
Synthetic Test

In engineering, metaheuristic algorithms have been used to solve complex optimization problems. This paper investigates and compares various algorithms. On one hand, the study seeks to ascertain the advantages and disadvantages of the newly presented heuristic techniques. The efficiency of the algorithms is highly dependent on the nature of the problem. The ability to change the complexity of the problem and the knowledge of global optimal locations are two advantages of using synthetic test functions for algorithm benchmarking. On the other hand, real-world design issues may frequently give more meaningful information into the effectiveness of optimization strategies. A new synthetic test function generator has been built to examine various optimization techniques. The objective function noisiness increased significantly with different transformations (Euclidean distance-based weighting, Gaussian weighting and Gabor-like weighting), while the positions of the optima remained the same. The test functions were created to assess and compare the performance of the algorithms in preparation for further development. The ideal proportions of the primary girder of an overhead crane have also been discovered. By evaluating the performance of fifteen metaheuristic algorithms, the optimum solution to thirteen mathematical optimization techniques, as well as the box-girder design, is identified. Some conclusions were drawn about the efficiency of the different optimization techniques at the test function and the transformed noisy functions. The overhead travelling crane girder design shows the real-life application.

scholarly journals Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Radu-Emil Precup ◽  
Emil-Ioan Voisan ◽  
Emil M. Petriu ◽  
Marius L. Tomescu ◽  
Radu-Codrut David ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Tracking Control ◽  
Fuzzy Controller ◽  
Optimization Problems ◽  
Cost Effective ◽  
Grey Wolf Optimizer ◽  
Reference Trajectory ◽  
Grey Wolf ◽  
Wheeled Mobile Robots

This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement.

scholarly journals On the Binarization of Grey Wolf Optimizer‎: ‎A Novel Binary Optimizer Algorithm

2021 ◽  
Author(s):  
Mehdy Roayaei
Keyword(s):  
Combinatorial Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Combinatorial Optimization Problems ◽  
Hunting Behaviour ◽  
Set Operations

Abstract ‎Grey Wolf Optimizer (GWO) is a population-based evolutionary algorithm inspired by the hunting behaviour of grey wolves‎. ‎GWO‎, ‎in its basic form‎, ‎is a real coded algorithm‎, ‎therefore‎, ‎it needs modifications to deal with binary optimization problems‎. ‎In this paper‎, ‎we review previous works on binarization of GWO‎, ‎and classify them with respect to their encoding scheme‎, ‎updating strategy‎, ‎and transfer function‎. ‎Then‎, ‎we propose a novel binary GWO algorithm (named SetGWO)‎, ‎which is based on set encoding and uses set operations in its updating strategy‎. ‎Experimental results on different real-world combinatorial optimization problems and different datasets‎, ‎show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions‎, ‎running time‎, ‎and scalability‎.

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