Drone Flocking Optimization using NSGA-II and Principal Component Analysis

Individual agents in natural systems like flocks of birds or schools of fish display a remarkable ability to coordinate and communicate in local groups and execute a variety of tasks efficiently. Emulating such natural systems into drone swarms to solve problems in defence, agriculture, industry automation and humanitarian relief is an emerging technology. However, flocking of aerial robots while maintaining multiple objectives, like collision avoidance, high speed etc. is still a challenge. In this paper, optimized flocking of drones in a confined environment with multiple conflicting objectives is proposed. The considered objectives are collision avoidance (with each other and the wall), speed, correlation, and communication (connected and disconnected agents). Principal Component Analysis (PCA) is applied for dimensionality reduction, and understanding the collective dynamics of the swarm. The control model is characterised by 12 parameters which are then optimized using a multi-objective solver (NSGA-II). The obtained results are reported and compared with that of the CMA-ES algorithm. The study is particularly useful as the proposed optimizer outputs a Pareto Front representing different types of swarms which can applied to different scenarios in the real world.

A binary variant of gravitational search algorithm and its application to windfarm layout optimization problem

In the binary search space, GSA framework encounters the shortcomings of stagnation, diversity loss, premature convergence and high time complexity. To address these issues, a novel binary variant of GSA called `A novel neighbourhood archives embedded gravitational constant in GSA for binary search space (BNAGGSA)' is proposed in this paper. In BNAGGSA, the novel fitness-distance based social interaction strategy produces a self-adaptive step size mechanism through which the agent moves towards the optimal direction with the optimal step size, as per its current search requirement. The performance of the proposed algorithm is compared with the two binary variants of GSA over 23 well-known benchmark test problems. The experimental results and statistical analyses prove the supremacy of BNAGGSA over the compared algorithms. Furthermore, to check the applicability of the proposed algorithm in solving real-world applications, a windfarm layout optimization problem is considered. Two case studies with two different wind data sets of two different wind sites is considered for experiments.