Estudo de algoritmos de otimização bio-inspirados aplica à segmentação de imagens

2020 ◽  
Author(s):  
◽  
N. T. Saito
Keyword(s):  
Optimization Algorithm ◽  
Differential Evolution Algorithm ◽  
Grey Wolf Optimizer ◽  
Extensive System ◽  
Bee Colony ◽  
Krill Herd ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
Nonadditive Entropy ◽  
Grasshopper Optimization

Image segmentation is one of the first steps within the framework for processing scenes. Among the main existing techniques, we highlight the histogram-based binarization, which due to the simplicity of understanding and low computational complexity is one of the most used methods. However, for a multi-threshold process, this method becomes computationally costly. To minimize this problem, optimization algorithms are used to find the best thresholds. Recently, several algorithms inspired by nature have been proposed in a generic way in the area of combinatorial optimization and obtained excellent results, among which we highlight the more traditional ones such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Krill Herd (KH). This work shows a comparison between some of these algorithms and more recent algorithms, from 2014, as Grey Wolf Optimizer (GWO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and Harris Hawks Optmization (HHO) . This work compared the thresholds obtained by 7 bio-inspired algorithms in a base composed of 100 images with 1 single object provided by the Weizmann Institute of Science (WIS). The comparison was made using consolidated metrics like Dice/Jaccard and PSNR, as well the recent Hxyz. In the experiments were used the extensive system as an objective function (Kapurs´ Method). Still in the proposal of this experiment, the extensive system was compared with a Tsallis nonadditive entropy, with the Super-extensive system being configured with q ? [0.1, 0.2, . . . 0.9] and the Sub-extensive system with q ? [1.1, 1.2, . . . 1.9]. The image Database contains 100 images with only 1 object on scene. The results show that the Krill Herd (KH) algorithm was the winning algorithm in 35% of executions according to the PSNR metric, 28% in the Dice/Jaccard metric and 35% on the Hxyz metric. The extensive system had the best overall performance and was responsible for the best threshold of 54 images according to the metric PSNR, 30 according to the metric Dice/Jaccard and 39 according to the Hxyz metric

scholarly journals Application of Grasshopper Optimization Algorithm for Selective Harmonics Elimination in Low-Frequency Voltage Source Inverter

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6426
Author(s):  
Marcin Steczek ◽  
Włodzimierz Jefimowski ◽  
Adam Szeląg
Keyword(s):  
Optimization Algorithm ◽  
Low Frequency ◽  
Insect Behavior ◽  
Voltage Source ◽  
Grey Wolf Optimizer ◽  
Voltage Source Inverter ◽  
Harmonic Elimination ◽  
Grasshopper Optimization Algorithm ◽  
Selective Harmonic Elimination ◽  
Grasshopper Optimization

In this paper, an application of the recently developed Grasshopper Optimization Algorithm (GOA) for calculation of switching angles for Selective Harmonic Elimination (SHE) PWM in low-frequency voltage source inverter is proposed. The algorithm is based on insect behavior in the food foraging swarm of grasshoppers. The characteristic feature of GOA is the movement of agents is based on the position of all agents in the swarm. This method represents a higher probability of convergence than Particle Swarm Optimization (PSO) Modifications of GOA have been examined regarding their effect on the algorithm’s convergence. The proposed modifications were based on the following techniques: Grey Wolf Optimizer (GWO), Natural Selection (NS), Adaptive Grasshopper Optimization Algorithm (AGOA), and Opposite Based Learning (OBL). The performance of GOA and its modifications were compared with well-known PSO. Areas, where GOA is superior to PSO in terms of probability of convergence, have been shown. The efficiency of the GOA algorithm applied for solving the SHE problem was confirmed by measurements in the laboratory.

scholarly journals Voltage control of switched reluctance generator using grasshopper optimization algorithm

2020 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Mohamed Bahy ◽  
Adel S. Nada ◽  
Sayed H. Elbanna ◽  
Mohamed A. M. Shanab
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Control Process ◽  
Voltage Control ◽  
Pi Controller ◽  
Switched Reluctance ◽  
Control Approach ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
Grasshopper Optimization

<p>This paper presents<strong> </strong>a terminal voltage control approach of a Switched Reluctance Generator (SRG) based wind turbine generating systems. The control process is employed using a closed loop stimulated by the error between the reference voltage and the generator output voltage due to load and wind speed variation. This error feeds the tuned Proportional Integral controller (PI).</p><p>Tuning of PI controller by conventional analysis methods is difficult by the existence of a significant non-linearity. A novel strategy method is presented here to determine optimum PI controller parameters of voltage control of SRG using Grasshopper Optimization Algorithm (GOA). This proposed approach is a simple and effective algorithm that is able to solve many optimization problems. The simplicity of algorithm provides high quality tuning of optimal PI controller parameters. The integral of time weighted squared error (ITSE) is used as the performance of the proposed GOA-PI controller. The effectiveness of the proposed strategy is tested with the three-phase 12/8 structure SRG. Outcomes indicate the supremacy of GOA over Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) in terms of control performance measures.</p>

Robust design of a robot gripper mechanism using new hybrid grasshopper optimization algorithm

2021 ◽  
Author(s):  
Betül Sultan Yildiz ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Ali Riza Yildiz ◽  
Sadiq M. Sait
Keyword(s):  
Robust Design ◽  
Optimization Algorithm ◽  
Robot Gripper ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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