In this paper, a general and integrated form is predefined goal. In the selective breeding or crossover proposed for the different kinds of particle swarm process, fit individuals are chosen to produce more optimization. Also, some related theoretical results are given, offspring than less fit individuals, which tends to including a convergence theorem for the random selection homogenize the population and improves the average result case and a lemma on probability percentile. To compare as the algorithm progresses. Subsequent mutations of the different PSO algorithms in effectiveness and efficiency, we offspring add diversity to the population and explore new propose three comparison indexes of universal standard.areas of the parameter search space. On a different side, the Particle Swarm Optimization I INTRODUCTION (PSO), introduced by Kennedy and Eberhart [9], [10], is a stochastic optimization technique that can be likened to theIn contrast to the traditional adaptive stochastic search behavior of a flock of birds or the sociological behavior of algorithms, evolutionary computation (EC) techniques a group of people. The PSO is a population based exploit a set of potential solutions, named a population, and optimization technique, where the population is called a detect the optimal solution through cooperation and swarm. A simple explanation of the PSO's operation is as competition among the individuals of the population. These follows. Each particle represents a possible solution to the techniques often detect optima in difficult optimization optimization task at hand. During iterations, each particle problems faster than traditional optimization methods. The accelerates in the direction of its own personal best solution most frequently encountered population-based EC found so far, as well as in the direction of the global best techniques, such as evolutionary programming [1], position discovered so far by any of the particles in the evolution strategies (ES) [2], [3], [4], genetic algorithms swarm. This means that if a particle discovers a promising (GAs) [5], [6], and genetic programming [7], [8], are new solution, all the other particles will move closer to it, inspired from the evolutionary mechanisms of nature. exploring the region more thoroughly in the process. The Among others, GAs are a family of computational PSO has been used to solve a range of optimization models inspired by the concept about natural evolution, problems, including neural network training [1 1] ,[12] , [13] Motivated by Darwin's theories of evolution and the and function minimization [14], [15].concept of "survival of the fittest,"' GAs use processes analogous to genetic recombination and mutation to II INTEGRATED PSO ALGORITHM promote the evolution of a population that best satisfies a
Oral cancer (OC) is one of the most common cancers worldwide, and its incidence has regional differences. In this study, the cancer registry database obtained from 1980 to 2019 was used to analyze the characteristic of incidence of OC by average annual percentage change (AAPC) and an age–period–cohort model. Spearman’s correlation was used to analyze the relationship between the age-standard incidence rates (ASR) of OC and related risk factors. Our results showed that the ASR of OC increased from 4.19 to 27.19 per 100,000 population with an AAPC of 5.1% (95% CI = 3.9–6.3, p value < 0.001) in men and from 1.16 to 2.8 per 100,000 population with an AAPC of 3.1% (95% CI = 2.6–3.6, p value < 0.001) in women between 1980–1984 and 2015–2019. The age–period–cohort model reported a trend of rising then declining for the rate ratio in men, with peaks occurring in the 1975 cohort, with a rate ratio of 6.80. The trend of incidence of oral cancer was related to changes in the consumption of cigarettes and alcohol and production of betel quid, with r values of 0.952, 0.979 and 0.963, respectively (all p values < 0.001). We strongly suggest avoiding these risk factors in order to prevent OC.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.