Learning in Particle Swarm Optimization
نویسندگان
چکیده
This paper presents particle swarm optimization based on learning from winner particle. (PSO-WS). Instead of considering gbest and pbest particle for position update, each particle considers its distance from immediate winner to update its position. Only winner particle follow general velocity and position update equation. If this strategy performs well for the particle, then that particle updates its position based on this strategy, otherwise its position is replaced by its immediate winner particle’s position. Dimension dependant swarm size is used for better exploration. Proposed method is compared with CSO and CCPSO2, which are available to solve large scale optimization problems. Statistical results show that proposed method performs well for separable as well as non separable problems.
منابع مشابه
Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW)
So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...
متن کاملStock Price Prediction using Machine Learning and Swarm Intelligence
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...
متن کاملOptimal Placement and Sizing of DGs and Shunt Capacitor Banks Simultaneously in Distribution Networks using Particle Swarm Optimization Algorithm Based on Adaptive Learning Strategy
Abstract: Optimization of DG and capacitors is a nonlinear objective optimization problem with equal and unequal constraints, and the efficiency of meta-heuristic methods for solving optimization problems has been proven to any degree of complex it. As the population grows and then electricity consumption increases, the need for generation increases, which further reduces voltage, increases los...
متن کاملAdaptive particularly tunable fuzzy particle swarm optimization algorithm
Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...
متن کاملAn Improved Particle Swarm Optimizer Based on a Novel Class of Fast and Efficient Learning Factors Strategies
The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and ex...
متن کاملS3PSO: Students’ Performance Prediction Based on Particle Swarm Optimization
Nowadays, new methods are required to take advantage of the rich and extensive gold mine of data given the vast content of data particularly created by educational systems. Data mining algorithms have been used in educational systems especially e-learning systems due to the broad usage of these systems. Providing a model to predict final student results in educational course is a reason for usi...
متن کامل