Particle Swarm
نویسنده
چکیده
Many training algorithms (like gradient descent, for example) use random initial weights. These algorithms are rather sensitive to their starting position in the error space, which is represented by their initial weights. This paper shows that the training performance can be improved signiicantly by using a Particle Swarm Optimizer (PSO) to initialize the weights, rather than random initialization.
منابع مشابه
A particle swarm optimization method for periodic vehicle routing problem with pickup and delivery in transportation
In this article, multiple-product PVRP with pickup and delivery that is used widely in goods distribution or other service companies, especially by railways, was introduced. A mathematical formulation was provided for this problem. Each product had a set of vehicles which could carry the product and pickup and delivery could simultaneously occur. To solve the problem, two meta-heuristic methods...
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 کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملResearch of Blind Signals Separation with Genetic Algorithm and Particle Swarm Optimization Based on Mutual Information
Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...
متن کاملDiversified Particle Swarm Optimization for Hybrid Flowshop Scheduling
The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society ...
متن کاملAN OPTIMAL FUZZY SLIDING MODE CONTROLLER DESIGN BASED ON PARTICLE SWARM OPTIMIZATION AND USING SCALAR SIGN FUNCTION
This paper addresses the problems caused by an inappropriate selection of sliding surface parameters in fuzzy sliding mode controllers via an optimization approach. In particular, the proposed method employs the parallel distributed compensator scheme to design the state feedback based control law. The controller gains are determined in offline mode via a linear quadratic regular. The particle ...
متن کامل