This article presents a novelimplementation of Particle Swarm Optimisation(PSO)forfinding the most optimal solution to path planning problem for a swarm of robots. The swarm canvasses through the configuration space having static obstaclesby applying PSO on potential fields generated by the target. The best possible path by the momentary leaders of the group is retraced toget the solution. The ...

Cancer chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumours using anti-cancer drugs with the adverse toxic side-effects caused by these drugs. Some methods of computational optimisation, Genetic Algorithms in particular, have proven to be useful in helping to strike the right balance. The purpose of this paper is to study how an alternative optimisat...

Adaptive infinite-impulse-response (IIR) filtering provides a powerful approach for solving a variety of practical signal processing problems. Because the error surface of IIR filters is typically multimodal, global optimisation techniques are generally required in order to avoid local minima. This contribution applies the particle swarm optimisation (PSO) to digital IIR filter design in a real...

in this paper, a new enhanced version of the particle swarm optimization (pso) is presented. an important modification is made by adding probabilistic functions into pso, and it is named probabilistic particle swarm optimization (ppso). since the variation of the velocity of particles in pso constitutes its search engine, it should provide two phases of optimization process which are: explorati...

In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would been used. This likely includes crisis response search rescue missions. These systems will need method navigating unknown dynamic environments. Typically, this require an incremental heuristic algor...

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...

Because of the existence irrelevant, redundant, and noisy attributes in large datasets, accuracy a classification model has degraded. Hence, feature selection is necessary pre-processing stage to select important features that may considerably increase efficiency underlying algorithms. As popular metaheuristic algorithm, particle swarm optimisation successfully applied various approaches. Never...