Search

This paper focuses on improving an optimization process for swarm robots using Particle Swarm Optimization (PSO) by altering the acceleration coefficient from static to dynamic. In swarm robotic, motion coordination addresses the issue of avoiding a group of robots interfere with each other in a limited workspace, while achieving the global motion objective. PSO is commonly suggested in the literature to optimize path trajectory in robotic field. However, the typical PSO tends to be trapped in local optima. Therefore, a dynamic acceleration coefficient is proposed to optimize the cognitive and social coefficients of PSO in order to improve its exploration ability in seeking the global optimum solution. With this novel feature, PSO becomes less dependent on the chain of its past experience that it had explored in a certain region within the solution space. The effectiveness of the proposed method is tested on a simulated swarm robotic platform. Results show the proposed PSO with Dynamic Coefficient (DCPSO) is 1.09 seconds and 3.58 seconds faster than the typical PSO under dynamic and extreme conditions respectively. [ABSTRACT FROM AUTHOR]

This paper presents particle swarm optimization based perturb and observe (PSO-P&O) algorithm for maximizing output power of photovoltaic (PV) array under partially shaded conditions (PSC). During PSC, the P-V characteristic of PV will become more complex with multiple maximum power points (MPP). Most of the conventional maximum power point tracking (MPPT) algorithms, such as P&O, will be trapped at the local MPP and hence limiting the maximum power generation. As such, investigation on PSO-P&O algorithm is carried out to maximize the PV generated power principally under PSC operation. The performances of conventional P&O and the proposed PSO-P&O algorithms are investigated particularly on the transient and steady state responses under various shaded conditions. The simulation results show the developed PSO- P&O algorithm is able to facilitate the PV array to reach the global MPP and assist the PV array to produce more stable output power compared to the conventional P&O algorithm. [ABSTRACT FROM AUTHOR]

Relieving urban traffic congestion has always been an urgent call in a dynamic traffic network. The objective of this research is to control the traffic flow within a traffic network consists of multiple signalized intersections with traffic ramp. The massive traffic network problem is dealt through Q-learning actuated traffic signalization (QLTS), where the traffic phases will be monitored as immediate actions can be taken during congestion to minimize the number of vehicles in queue. QLTS is tested under two cases and has better performance than common fixed-time traffic signalization (FTS). When dealing with the ramp flow, QLTS has flexibility to change the traffic signals according to the traffic conditions and necessity. [ABSTRACT FROM AUTHOR]