An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants
نویسندگان
چکیده مقاله:
Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSO-based approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration.
منابع مشابه
an expert system for intelligent selection of proper particle swarm optimization variants
regarding the large number of developed particle swarm optimization (pso) algorithms and the various applications for which pso has been used, selecting the most suitable variant of pso for solving a particular optimization problem is a challenge for most researchers. in this paper, using a comprehensive survey and taxonomy on different types of pso, an expert system (es) is designed to identif...
متن کاملAn Intelligent Particle Swarm Optimization for Fuzzy Based Heterogeneous Radio Access Technology (RAT) Selection
متن کامل
SELECTION OF SUITABLE RECORDS FOR NONLINEAR ANALYSIS USING GENETIC ALGORITHM (GA) AND PARTICLE SWARM OPTIMIZATION (PSO)
This paper presents a suitable and quick way to choose earthquake records in non-linear dynamic analysis using optimization methods. In addition, these earthquake records are scaled. Therefore, structural responses of three different soil-frame models were examined, the change in maximum displacement of roof was analyzed and the damage index of whole structures was measured. The soil classifica...
متن کاملComparison of particle swarm optimization and tabu search algorithms for portfolio selection problem
Using Metaheuristics models and Evolutionary Algorithms for solving portfolio problem has been considered in recent years.In this study, by using particles swarm optimization and tabu search algorithms we optimized two-sided risk measures . A standard exact penalty function transforms the considered portfolio selection problem into an equivalent unconstrained minimization problem. And in final...
متن کاملAn improved particle swarm optimization for feature selection
Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rule...
متن کاملParallel Implementation of Particle Swarm Optimization Variants Using Graphics Processing Unit Platform
There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The go...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 2 شماره 1
صفحات 569- 594
تاریخ انتشار 2015-05
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
کلمات کلیدی برای این مقاله ارائه نشده است
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023