scholarly journals Gray wolf optimizer with bubble-net predation for modeling FCCU main fractionator

2021 ◽  
Author(s):  
Xiaojing Wang ◽  
Chengli Su ◽  
Ning Wang ◽  
Huiyuan Shi
Keyword(s):  
Complex System ◽  
Parameter Estimation ◽  
Optimization Problem ◽  
Local Optimum ◽  
Gray Wolf ◽  
Test Functions ◽  
Gray Wolf Optimizer ◽  
Fractionation Model ◽  
Complex Optimization ◽  
Typical Test

Abstract FCCU main fractionator is a complex system with multivariable, nonlinear and uncertainty. Its modeling is a hard nut to crack. In this work, the gray wolf optimization with bubble-net predation (GWO_BP) is proposed for solving this complex optimization problem. In order to enhance the global search ability and accelerate the convergence speed, the bubble-net predation of whale search scheme is applied to update the head wolf position. And the improved Lé vy flight is used to update the positions of wolfpack for overcoming the disadvantage of easily falling into local optimum. The GWO_BP is compared with basic GWO, PSO with some typical test functions and the parameter estimation of FCCU main fractionation model. The experiment results show the effectiveness of the GWO_BP.

scholarly journals Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3029
Author(s):  
Yichen Zhou ◽  
Xiaohui Yang ◽  
Lingyu Tao ◽  
Li Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Probabilistic Neural Network ◽  
Diagnostic Methods ◽  
Local Optimum ◽  
Gray Wolf ◽  
Gray Wolf Optimizer ◽  
Diagnosis Model ◽  
Transformer Fault

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis.

scholarly journals MLO: Multi Leader Optimizer

2020 ◽  
Vol 13 (6) ◽  
pp. 364-373
Author(s):  
Mohammad Dehghani ◽  
◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Ricardo Ramirez-Mendoza ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Emperor Penguin ◽  
Gray Wolf ◽  
Test Functions ◽  
Gray Wolf Optimizer ◽  
Grasshopper Optimization Algorithm ◽  
Teaching Learning Based Optimization ◽  
Grasshopper Optimization ◽  
Teaching Learning

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

scholarly journals Implementation of combined new optimal cuckoo algorithm with a gray wolf algorithm to solve unconstrained optimization nonlinear problems

2020 ◽  
Vol 19 (3) ◽  
pp. 1582
Author(s):  
Ali Abbas Al-Arabo ◽  
Rana Zaidan Alkawaz
Keyword(s):  
Nonlinear Problems ◽  
Minimum Point ◽  
Test Problems ◽  
Gray Wolf ◽  
Test Functions ◽  
Local Minimum Point ◽  
Gray Wolf Optimizer ◽  
Nature Inspired Algorithm ◽  
Combined Algorithm ◽  
Cuckoo Algorithm

<p>In this article, a combined optimization algorithm was proposed which combines the optimal adaptive Cuckoo algorithm (OACS) which is Nature-inspired algorithm with Gray Wolf optimizer algorithm (GWO). Sometimes considering the cuckoo algorithm alone, may fail to find the local minimum-point and also fails to reach to the solution because of the slow speed of its convergence property. Therefore, considering the new proposed adaptive combined algorithm gave a strong improvement for using this to reach the minimum point in solving (23) nonlinear test problems. This is suitable to solve a large number of nonlinear unconstraint optimization test functions with obtaining good and robust numerical results.</p>

scholarly journals A Modified Niching Crow Search Approach to Well Placement Optimization

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 857
Author(s):  
Jahedul Islam ◽  
Md Shokor A. Rahaman ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Ahshanul Hoqe ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multimodal Optimization ◽  
Test Functions ◽  
Well Placement ◽  
Suggested Approach ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Search Approach

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

scholarly journals A Multiple Mitosis Genetic Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 252
Author(s):  
K. Kamil ◽  
K.H Chong ◽  
H. Hashim ◽  
S.A. Shaaya
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Total Population ◽  
Global Optimum ◽  
Natural Process ◽  
Local Optimum ◽  
Simple Genetic Algorithm ◽  
Cell Reproduction ◽  
Solve Optimization Problem ◽  
High Diversity

<p>Genetic algorithm is a well-known metaheuristic method to solve optimization problem mimic the natural process of cell reproduction. Having great advantages on solving optimization problem makes this method popular among researchers to improve the performance of simple Genetic Algorithm and apply it in many areas. However, Genetic Algorithm has its own weakness of less diversity which cause premature convergence where the potential answer trapped in its local optimum.  This paper proposed a method Multiple Mitosis Genetic Algorithm to improve the performance of simple Genetic Algorithm to promote high diversity of high-quality individuals by having 3 different steps which are set multiplying factor before the crossover process, conduct multiple mitosis crossover and introduce mini loop in each generation. Results shows that the percentage of great quality individuals improve until 90 percent of total population to find the global optimum.</p>

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