Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. Although it has been shown to perform well in finding optimal solutions for many optimization problems, there has so far been little theoretical analysis on its convergence and performance. This p...

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the ...

Dieser Erfahrungsbericht schildert Erfahrungen mit der Grundausbildung in Programmiersprachen. Soweit es ging, konnten Studenten aktiv lernen. Auch die Leistungskontrolle sollte einen aktiven Charakter bekommen: gleichzeitig von den Studenten aktiv erlebt und gestaltet werden, ihnen aber auch Feedback geben und am Ende benotet werden. Dabei erwies sich die klassische Klausur als ungeeignet. Der...

This paper presents a new optimization algorithm – MCPSO, multi-swarm cooperative particle swarm optimizer, inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master–slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, w...

Simple and unified non-operational specifications of the three memory consistency models Total Store Ordering (TSO), Partial Store Ordering (PSO), and Relaxed Memory Order (RMO) of SPARC multiprocessors are presented and proved correct. The specifications are intuitive partial order constraints on possible computations and are derived from natural successive weakening of Lamport’s Sequential Co...

This paper presents an algorithm for solving optimal power flow problem through the application of Differential Evolution (DE). Economic Dispatch is the process of allocating the required load demand between the available generation units such that the cost of operation is minimized. There have been many algorithms proposed for economic dispatch out of which a Differential Evolution (DE) is dis...

The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay cognitive radio networks (CRNs). Moving forward towards the realization Fifth Generation (5G) beyond, where optimization envisioned to take place multiple performance dimensions, it crucially desirable achieve high sum throughput with low consumption. In this work, multi-objective JPAC that joint...

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is overcome major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around near optimal solutions. first proposed variant incorporates four key operations, including a modified operation with rectified personal global best signals, spi...

Extending the particle swarm optimization (PSO) algorithm to be one of systemic modeling and controlling tools, several research groups investigate target search with swarm robots (simulated or physical) respectively (Doctor et al., 2004; Hereford & Siebold, 2008; Jatmiko et al., 2007; Marques et al., 2006; Pugh & Martinoli, 2007; Xue & Zeng, 2008). The common idea they hold is to map such swar...

Particle Swarm Optimization (PSO) is a popular nature-inspired meta-heuristic for solving continuous optimization problems. Although this technique is widely used, the understanding of the mechanisms that make swarms so successful is still limited. We present the first substantial experimental investigation of the influence of the local attractor on the quality of exploration and exploitation. ...