Abstract: |
This paper proposes a new Hybrid Particle Swarm Optimizer model based on breeding concepts from novel evolutionary algorithms. The hybrid PSO combines traditional velocity and position update rules of RANDIW-PSO and ideas from Self Adaptive Pareto Differential Evolution Algorithm (SPDE). The hybrid model is tested and compared with some high quality PSO models like the RANDIW-PSO and TVIW-PSO. The results indicate two good prospects of our proposed hybrid PSO model: potential to achieve faster convergence as well as potential to find a better solution. We obtain outstanding performance on solving single objective problems in comparison to several other PSO models. The hybrid PSO model, with the abovementioned features, is then efficiently utilized in the problem of coordinating a system of robot ants in order to help them to probe as much camera coverage area of some planetary surface or working field as possible, with minimum common area coverage. Thus by the Hybrid PSO, the total intersecting area of camera coverage regions of the robots is minimized quickly and efficiently, which is suited for online real world applications. |
