A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-designed PSO algorithm by using ten artifi...

Particle swarm optimization (PSO) has shown its good search ability in many optimization problems. However, PSO often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO, namely LSPSO, to solve this problem by employi...

Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, in some domains even well-established techniques such as Particle Swarm Optimization (PSO) may not present the necessary ability to generate diversity during the process of the swarm convergence. Indeed, this is the major difficulty to use PSO to tackle dynamic problems. Many efforts to overcome...

Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed approach is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm. We propose...

QoS multicast routing is a non-linear combinatorial optimization problem that arises in many multimedia applications. Providing QoS support is crucial to guarantee effective transportation of multimedia service in multicast communication. Computing the band-widthdelay constrained least cost multicast routing tree is an NP-complete problem. In this paper, a novel heuristic QoS multicast routing ...