In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a trigger...

In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization...

A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for m...

Using Particle Swarm Optimization to handle complex functions with high-dimension it has the problems of low convergence speed and sensitivity to local convergence. The convergence of particle swarm algorithm is studied, and the condition for the convergence of particle swarm algorithm is given. Results of numerical tests show the efficiency of the results. Base on the idea of specialization an...

Particle swarm optimization (PSO) is a Swarm Intelligence technique used for optimization motivated by the social behavior of individuals in large groups in nature. The damped mass-spring analogy known as the PSO continuous model allowed us to derive a whole family of particle swarm optimizers with different properties with regard to their exploitation/exploration balance. Using the theory of s...

This paper develops an efficient and clear insight view about the application of various PSO algorithms to the economic load dispatch problem with emission restrictions as the constraint. Solution acceleration techniques in the algorithm which enhance the speed and robustness of the algorithm are developed. The power and usefulness of the algorithm is demonstrated through its application to a t...

In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. In this paper, motivated by the behavior and the spatial characteristics of the social and cognitive experience of eachparticle in the swarm,wedevelop ahybrid framework that combines theParticle Swarm Optimization and the Differentia...

Artificial Neural Network (ANN) has found widespread application in the field of classification. Many domains have benefited with the use of ANN based models over traditional statistical models for their classification and prediction needs. Many techniques have been proposed to arrive at optimal values for parameters of the ANN model to improve its prediction accuracy. This paper compares the i...

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

Back propagation algorithm (BPA) have the complexity, local minima problem so we are using Particle Swarm optimization (PSO) algorithms to reduce and optimize BPA. In this paper, two variants of Particle Swarm Optimization (PSO) PSO_Hill and PSO_A* is used as optimization algorithm. PSO_Hill and PSO_A* algorithms are analyzed and evaluated on the basis of their advantages, applied to feed forwa...