In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.
The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.