Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing stateof-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algori...

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...

Particle swarm optimization (PSO) has recently been modified to several versions. Heterogeneous PSO is a recent extension which includes behavioral heterogeneity of particles. Here we propose a further developed version that has hierarchical interaction patterns among heterogeneous particles, which we call hierarchical heterogeneous PSO (HHPSO). Two algorithm designs that have been developed an...

Motivation, BRCA1 and BRCA2 are genes with tumor suppressor activity. They involved in a considerable number of biological processes. To help the biologist classification, we developed deep learning algorithm. The question when want to construct neural network is how many hidden layers neurons should use. If inputs outputs defined by problem, difficult define. Hidden that make up each layer inf...

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 ...

69 Abstract—In this paper two novel Particle Swarm Optimization (PSO)-based algorithms are presented for robot path planning with respect to two objectives, the shortest and smoothest path criteria. The first algorithm is a hybrid of the PSO and the Probabilistic Roadmap (PRM) methods, in which the PSO serves as the global planner whereas the PRM performs the local planning task. The second alg...

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic commun...