An Adaptive Population-based Simplex Method for Continuous Optimization

2016 ◽  
Vol 7 (4) ◽  
pp. 23-51 ◽  
Author(s):  
Mahamed G.H. Omran ◽  
Maurice Clerc
Keyword(s):  
Simplex Method ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Test Functions ◽  
Dimensional Simplex ◽  
Low Dimensional ◽  
Better Than ◽  
Real World Problems

This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.

scholarly journals Adaptive Opposition Slime Mould Algorithm

2021 ◽  
Author(s):  
Manoj Kumar Naik ◽  
Rutuparna Panda ◽  
Ajith Abraham
Keyword(s):  
Random Search ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Function Optimization ◽  
Rank Test ◽  
Test Functions ◽  
Slime Mould ◽  
Opposition Based Learning ◽  
Suggested Technique ◽  
Better Than

Abstract Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition based learning (OBL) will be used or not. Sometimes the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA’s performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon’s rank-sum test. It also ranked one in Friedman’s mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems.

scholarly journals Self-Adaptive Artificial Bee Colony for Function Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhu Tang ◽  
Wen Long ◽  
Huawei Wu ◽  
Kang Zhang ◽  
Yuri A. W. Shardt
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Initial Population ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Global And Local ◽  
Self Adaptive

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

scholarly journals Optimizing the composition of LiFSI-based electrolytes by a method combining simplex with normalization

RSC Advances ◽  
2021 ◽  
Vol 11 (42) ◽  
pp. 26102-26109
Author(s):  
Hongli Lu ◽  
Shuangwei Zeng ◽  
Dongni Zhao ◽  
Jie Wang ◽  
Yin Quan ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Simplex Method ◽  
Lithium Ion ◽  
Electrolyte System ◽  
Electrochemical Testing ◽  
Better Than

The manuscript addresses that the electrolyte system with five components was optimized by combining the simplex method, normalization and electrochemical testing in lithium-ion batteries. The optimized electrolyte is better than commercial electrolyte LiPF6–EC/DEC.

scholarly journals An enhanced chimp optimization algorithm for continuous optimization domains

2021 ◽  
Author(s):  
Heming Jia ◽  
Kangjian Sun ◽  
Wanying Zhang ◽  
Xin Leng
Keyword(s):  
Social Status ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Rank Correlation ◽  
Function Optimization ◽  
Local Optimum ◽  
Hunting Behavior ◽  
Spearman’S Rank Correlation ◽  
Engineering Design Problems ◽  
Application Fields

AbstractChimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

A Mathematical Principle and Application on Design of Planar Six-Bar Mechanism with unto Three-Ordered Intermission at Limit Position(s)

2010 ◽  
Vol 37-38 ◽  
pp. 9-13
Author(s):  
Hong Xin Wang ◽  
Ning Dai
Keyword(s):  
Design Method ◽  
Optimization Method ◽  
Basic Function ◽  
Mathematical Principle ◽  
Transmission Characteristics ◽  
Basic Functions ◽  
Combined Mechanism ◽  
Limit Position ◽  
Derivatives Of ◽  
Better Than

A non-iterative design method about high order intermittent mechanisms is presented. The mathematical principle is that a compound function produced by two basic functions, and then one to three order derivatives of the compound function are all zeroes when one order derivative of each basic function is zero at the same moment. The design method is that a combined mechanism is constructed by six bars; the displacement functions of the front four-bar and back four-bar mechanisms are separately built, let one order derivatives of two displacement functions separately be zero at the same moment, and then get geometrical relationships and solution on the intermittent mechanism. A design example shows that this method is simpler and transmission characteristics are better than optimization method.

Analyzing self-★ island-based memetic algorithms in heterogeneous unstable environments

2016 ◽  
Vol 32 (5) ◽  
pp. 676-692 ◽  
Author(s):  
Rafael Nogueras ◽  
Carlos Cotta
Keyword(s):  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Population Based ◽  
Self Healing ◽  
Computational Power ◽  
Ability To Work ◽  
Computing Power ◽  
Simulated Environment ◽  
Computational Resources ◽  
Better Than

Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that is, different distributions of computational power among computing nodes), and we study different strategies for distributing the search among nodes. We observe that the addition of self-scaling and self-healing properties makes the memetic algorithm very robust to both system instability and computational heterogeneity. Additionally, a strategy based on distributing single islands on each computational node is shown to perform globally better than placing many such islands on each of them (either proportionally to their computing power or subject to an intermediate compromise).

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

scholarly journals Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

2017 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Vina Ayumi ◽  
L.M. Rasdi Rere ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Contrast Enhancement ◽  
Harmony Search ◽  
Search Space ◽  
Optimization Method ◽  
Large Solution ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Simulation Results ◽  
Better Than

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

scholarly journals The performance of anthropometric measures to predict diabetes mellitus and hypertension among adults in Jordan

2019 ◽  
Author(s):  
Yousef Khader ◽  
Anwar Batieha ◽  
Hashem Jaddou ◽  
Mohammad El-Khateeb ◽  
Kamel Ajlouni
Keyword(s):  
Adult Population ◽  
Population Based ◽  
Hip Circumference ◽  
Anthropometric Measures ◽  
Study Objective ◽  
Biochemical Measurements ◽  
Diagnosed Diabetes ◽  
Newly Diagnosed Diabetes ◽  
Better Than ◽  
Structured Questionnaire

Abstract Objectives This study aimed to evaluate and compare the abilities of waist circumference (WC), body mass index (BMI), hip circumference (HC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) to predict recently and previously diagnosed diabetes and hypertension and assess their appropriate cut-off values among Jordanian adults.Methods Data from the 2017 cardiovascular risk factors survey were analyzed to achieve the study objective. The survey collected extensive data from a national population-based sample of Jordanian residents. A structured questionnaire was used to collect sociodemographic variables and clinical data. Blood samples were taken for biochemical measurements. Anthropometric characteristics were measured by the same team of trained field researchers.Results This study included a total of 1193 men and 2863 women. Their age ranged from 18 to 90 year with a mean (SD) of 43.8 (14.2) year. WHtR performed better than other anthropometric measures and had a good accuracy (AUC>0.80) among women and fair accuracy among men to predict newly diagnosed diabetes and previously diagnosed diabetes and hypertension. The appropriate cut-off points for anthropometric measures among women were 92 cm form WC, 104 cm for HC, 30 Kg/m 2 for BMI, 0.85 for WHR, and 0.60 for WHtR. For men, the appropriate cut-off points were 100 cm for WC, 104 cm for HC, 27 Kg/m 2 for BMI, 0.93 for WHR, and 0.57 for WHtR.Conclusion WHtR performed better than other anthropometric measures in predicting diabetes and hypertension among adult population in Jordan. We recommend WHtR as a measure of choice with a cut-off value of 0.6 for women and 0.57 for men to predict diabetes and hypertension among Jordanians.

Optimization of Fluidized Bed Reactor of Oxidative Coupling of Methane

2012 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Mohammad-Hosein Eghbal-Ahmadi ◽  
Masoud Zaerpour ◽  
Mahdi Daneshpayeh ◽  
Navid Mostoufi
Keyword(s):  
Fluidized Bed ◽  
Oxidative Coupling ◽  
Operating Temperature ◽  
Fluidized Bed Reactor ◽  
Optimization Method ◽  
Oxidative Coupling Of Methane ◽  
Process Variables ◽  
Algorithm Optimization ◽  
Reactor Performance ◽  
Better Than

Optimization of process variables in the oxidative coupling of methane (OCM) over Mn/Na2WO4/SiO2 catalyst in a fluidized bed reactor was carried out. Effects of operating temperature, distribution pattern of oxygen injected to the reactor and the number of injections on the reactor performance on C2 (ethane + ethylene) yield were investigated. Process variables for one, two and three secondary oxygen injections were investigated to obtain the maximum C2 yield by genetic algorithm optimization method. The maximum C2 selectivity and yield of 47.1% and 22.87%, respectively, were achieved for three secondary oxygen injections at operating temperature of 746.05°C. The C2 yield achieved in this study is approximately 4% better than previous works reported in literature while the optimum temperature is lower.

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