Evaluation of several initialization methods on arithmetic optimization algorithm performance

2021 ◽  
Vol 31 (1) ◽  
pp. 70-94
Author(s):  
Jeffrey O. Agushaka ◽  
Absalom E. Ezugwu
Keyword(s):  
Population Size ◽  
Optimization Algorithm ◽  
Beta Distribution ◽  
Random Number ◽  
Large Population ◽  
Latin Hypercube Sampling ◽  
Rayleigh Distribution ◽  
Population Based ◽  
Algorithm Performance ◽  
Number Of Iterations

Abstract Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments.

scholarly journals Random search with k -prototypes algorithm for clustering mixed datasets

2011 ◽  
Vol 467 (2132) ◽  
pp. 2387-2403 ◽  
Author(s):  
Duc-Truong Pham ◽  
Maria M. Suarez-Alvarez ◽  
Yuriy I. Prostov
Keyword(s):  
Optimization Algorithm ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
The Other ◽  
Bees Algorithm ◽  
Neighbourhood Search ◽  
Promising Solution ◽  
Number Of Iterations ◽  
Selection Of

A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k -prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k -prototypes algorithm. The BA works with elite and good solutions, and continues to look for other possible extrema solutions keeping the number of testing points constant. However, the improvement of promising solutions by the BA may be time-consuming because it is based on random neighbourhood search. On the other hand, an application of the k -prototypes algorithm to a promising solution may be very effective because it improves the solution at each iteration. The RANKPRO algorithm balances two objectives: it explores the search space effectively owing to random selection of new solutions, and improves promising solutions fast owing to employment of the k -prototypes algorithm. The efficiency of the new algorithm is demonstrated by clustering several datasets. It is shown that in the majority of the considered datasets when the average number of iterations that the k -prototypes algorithm needs to converge is over 10, the RANKPRO algorithm is more efficient than the k -prototypes algorithm.

scholarly journals A Probabilistic Analysis of a Simplified Biogeography-Based Optimization Algorithm

2011 ◽  
Vol 19 (2) ◽  
pp. 167-188 ◽  
Author(s):  
Dan Simon
Keyword(s):  
Probability Theory ◽  
Evolutionary Algorithm ◽  
Population Size ◽  
Optimization Algorithm ◽  
Probabilistic Analysis ◽  
Population Based ◽  
Approximate Analysis ◽  
Expected Number ◽  
Expected Values ◽  
Biological Organisms

Biogeography-based optimization (BBO) is a population-based evolutionary algorithm (EA) that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. We present a simplified version of BBO and perform an approximate analysis of the BBO population using probability theory. Our analysis provides approximate values for the expected number of generations before the population's best solution improves, and the expected amount of improvement. These expected values are functions of the population size. We quantify three behaviors as the population size increases: first, we see that the best solution in the initial randomly generated population improves; second, we see that the expected number of generations before improvement increases; and third, we see that the expected amount of improvement decreases.

Associations between Body Composition and Mortality: A Large Population-Based Cohort Study

2020 ◽  
Author(s):  
Fu-Rong Li ◽  
Pei-Liang Chen ◽  
Xin Cheng ◽  
Hai-Lian Yang ◽  
Wen-Fang Zhong ◽  
...  
Keyword(s):  
Body Composition ◽  
Cohort Study ◽  
Large Population ◽  
Population Based

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

Fracture Risk in Patients with Myasthenia Gravis: A Population-Based Cohort Study

2021 ◽  
pp. 1-8
Author(s):  
Charles Kassardjian ◽  
Jessica Widdifield ◽  
J. Michael Paterson ◽  
Alexander Kopp ◽  
Chenthila Nagamuthu ◽  
...  
Keyword(s):  
Myasthenia Gravis ◽  
Lower Risk ◽  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Osteoporosis Prevention ◽  
Inception Cohort ◽  
Hazard Ratios ◽  
Multiple Covariates ◽  
Region Of Residence

Background: Prednisone is a common treatment for myasthenia gravis (MG), and osteoporosis is a known potential risk of chronic prednisone therapy. Objective: Our aim was to evaluate the risk of serious fractures in a population-based cohort of MG patients. Methods: An inception cohort of patients with MG was identified from administrative health data in Ontario, Canada between April 1, 2002 and December 31, 2015. For each MG patient, we matched 4 general population comparators based on age, sex, and region of residence. Fractures were identified through emergency department and hospitalization data. Crude overall rates and sex-specific rates of fractures were calculated for the MG and comparator groups, as well as rates of specific fractures. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox regression. Results: Among 3,823 incident MG patients (followed for a mean of 5 years), 188 (4.9%) experienced a fracture compared with 741 (4.8%) fractures amongst 15,292 matched comparators. Crude fracture rates were not different between the MG cohort and matched comparators (8.71 vs. 7.98 per 1000 patient years), overall and in men and women separately. After controlling for multiple covariates, MG patients had a significantly lower risk of fracture than comparators (HR 0.74, 95% CI 0.63–0.88). Conclusions: In this large, population-based cohort of incident MG patients, MG patients were at lower risk of a major fracture than comparators. The reasons for this finding are unclear but may highlight the importance osteoporosis prevention.

scholarly journals Estimating the asymptomatic proportion of SARS-CoV-2 infection in the general population: Analysis of nationwide serosurvey data in the Netherlands

2021 ◽  
Author(s):  
Scott A. McDonald ◽  
Fuminari Miura ◽  
Eric R. A. Vos ◽  
Michiel van Boven ◽  
Hester E. de Melker ◽  
...  
Keyword(s):  
The Netherlands ◽  
Population Analysis ◽  
Large Population ◽  
Population Based ◽  
Attributable Risk ◽  
Case Definition ◽  
Aggregated Data ◽  
Age Dependent ◽  
Additive Regression ◽  
June July

Abstract Background The proportion of SARS-CoV-2 positive persons who are asymptomatic—and whether this proportion is age-dependent—are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or 'crude' proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. Methods Based on two rounds of a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May and June/July 2020 in the Netherlands (n = 7517), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. Results Using age-aggregated data, the 'crude' AP was 37% but the model-estimated AP was 65% (95% CI 63–68%). The estimated AP varied with age, from 74% (95% CI 65–90%) for < 20 years, to 61% (95% CI 57–65%) for the 50–59 years age-group. Conclusion Whereas the 'crude' AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.

scholarly journals From Multiplex Serology to Serolomics—A Novel Approach to the Antibody Response against the SARS-CoV-2 Proteome

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 749
Author(s):  
Julia Butt ◽  
Rajagopal Murugan ◽  
Theresa Hippchen ◽  
Sylvia Olberg ◽  
Monique van Straaten ◽  
...  
Keyword(s):  
Antibody Response ◽  
High Throughput ◽  
Large Population ◽  
High Sensitivity ◽  
Population Based ◽  
Antibody Responses ◽  
Hek293 Cells ◽  
Novel Approach ◽  
Assay Performance ◽  
Multiplex Serology

The emerging SARS-CoV-2 pandemic entails an urgent need for specific and sensitive high-throughput serological assays to assess SARS-CoV-2 epidemiology. We, therefore, aimed at developing a fluorescent-bead based SARS-CoV-2 multiplex serology assay for detection of antibody responses to the SARS-CoV-2 proteome. Proteins of the SARS-CoV-2 proteome and protein N of SARS-CoV-1 and common cold Coronaviruses (ccCoVs) were recombinantly expressed in E. coli or HEK293 cells. Assay performance was assessed in a COVID-19 case cohort (n = 48 hospitalized patients from Heidelberg) as well as n = 85 age- and sex-matched pre-pandemic controls from the ESTHER study. Assay validation included comparison with home-made immunofluorescence and commercial enzyme-linked immunosorbent (ELISA) assays. A sensitivity of 100% (95% CI: 86–100%) was achieved in COVID-19 patients 14 days post symptom onset with dual sero-positivity to SARS-CoV-2 N and the receptor-binding domain of the spike protein. The specificity obtained with this algorithm was 100% (95% CI: 96–100%). Antibody responses to ccCoVs N were abundantly high and did not correlate with those to SARS-CoV-2 N. Inclusion of additional SARS-CoV-2 proteins as well as separate assessment of immunoglobulin (Ig) classes M, A, and G allowed for explorative analyses regarding disease progression and course of antibody response. This newly developed SARS-CoV-2 multiplex serology assay achieved high sensitivity and specificity to determine SARS-CoV-2 sero-positivity. Its high throughput ability allows epidemiologic SARS-CoV-2 research in large population-based studies. Inclusion of additional pathogens into the panel as well as separate assessment of Ig isotypes will furthermore allow addressing research questions beyond SARS-CoV-2 sero-prevalence.

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