People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem of how to choose a combination of data processing tools and algorithms for a given dataset. This problem is usually termed the Full Model Selection (FMS) problem. Extended fr...

Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This p...

A novel condensed polynomial neural network using particle swarm optimization (PSO) technique is proposed for the task of classification in this paper. In solving classification task classical algorithms such as polynomial neural network (PNN) and its variants need more computational time as the partial descriptions (PDs) grow over the training period layer-by-layer and make the network very co...

Abstract In this paper, a novel proportion-integral-derivative-like particle swarm optimization (PIDLPSO) algorithm is presented with improved terminal convergence of the dynamics. A derivative control term introduced into traditional (PSO) so as to alleviate overshoot problem during stage convergence. The velocity updated according past momentum, present positions (including personal best posi...

The continuous PSO algorithm has been widely researched and also applied as an intelligent computational technique to solve problems requiring iterative solutions based on some predefined objective function. However, the research on binary version of PSO (DBPSO) is still underway. The major research concerns are to accelerate the convergence speed retaining the search ability and reliability of...

Clustering of short-text collections is a very relevant research area, given the current and future mode for people to use “small-language” (e.g. blogs, snippets, news and text-message generation such as email or chat). In recent years, a few approaches based on Particle Swarm Optimization (PSO) have been proposed to solve document clustering problems. However, the particularities that arise wh...

This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected multiple threats. A cost function is first formulated convert into an that incorporates requirements and constraints feasible safe operation UAV. SPSO then used find optimal minimiz...

Abstract This paper investigates the performance of several meta-heuristic algorithms, including Particle Swarm Optimisation (PSO), different variants Differential Evolution (DE), Biogeography-Based (BBO), Cultural Algorithm (CA), Optics-Inspired (OIO), and League Championship (LCA), for optimum layup laminated composite plates. The study provides detailed Pseudo codes algorithms. buckling capa...

The particle swarm optimization (PSO) is a wide used algorithm, which yet suffers from trapping in local optimum and the premature convergence. Many studies have proposed improvements to address drawbacks above. Most of them implemented single strategy for one problem or fixed neighborhood structure during whole search process. To further improve PSO performance, we introduced simple but effect...

Particle swarm optimization (PSO) is a population based statistical optimization technique which is inspired by social behavior of bird flocking or fish schooling. The main weakness of PSO especially in multimodal problems is trapping in local minima. Recently a learning automata based PSO called PSO-LA to improve the performance of PSO has been reported. PSO-LA uses one learning automaton for ...