IHSSAO: An Improved Hybrid Salp Swarm Algorithm and Aquila Optimizer for UAV Path Planning in Complex Terrain

Abstract

In this paper, we propose a modified hybrid Salp Swarm Algorithm (SSA) and Aquila Optimizer (AO) named IHSSAO for UAV path planning in complex terrain. The primary logic of the proposed IHSSAO is to enhance the performance of AO by introducing the leader mechanism of SSA, tent chaotic map, and pinhole imaging opposition-based learning strategy. Firstly, the tent chaotic map is utilized to substitute the randomly generated initial population in the original algorithm to increase the diversity of the initial individuals. Secondly, we integrate the leader mechanism of SSA into the position update formulation of the basic AO, which enables the search individuals to fully utilize the optimal solution information and enhances the global search capability of AO. Thirdly, we introduce the pinhole imaging opposition-based learning in the proposed IHSSAO to enhance the capability to escape from the local optimization. To verify the effectiveness of the proposed IHSSAO algorithm, we tested it against SSA, AO, and five other advanced meta-heuristic algorithms on 23 classical benchmark functions and 17 IEEE CEC2017 test functions. The experimental results indicate that the proposed IHSSAO is superior to the other seven algorithms in most cases. Eventually, we applied the IHSSAO, SSA, and AO to solve the UAV path planning problem. The experimental results verify that the IHSSAO is superior to the basic SSA and AO for solving the UAV path planning problem in complex terrain.

Adjusting the Stiffness of Supports during Milling of a Large-Size Workpiece Using the Salp Swarm Algorithm

“…Some applications, mostly related to energy distribution and production systems, include, for example, optimization of wind turbine location [ 59 ], optimization of power system operations [ 60 ], estimation of the parameters of photovoltaic panels [ 61 ], and prediction of wind power [ 62 ]. Some other examples include UAV path planning [ 63 ], design of PID-fuzzy control against an earthquake for a seismic-exited structural system [ 64 ], and prediction of pressure burst in pipelines [ 65 ]. In all these tasks SSA showed very good performance, efficiency, and competitiveness, outperforming other well-established approaches [ 39 , 66 ].…”

Optimization of Skipping Rope Training Method Based on Chaotic Logistics

“…e results show that the combined analysis of chaos theory and the Apriori algorithm is better, and all indexes are significantly better than the Apriori algorithm [4]. e reason is that the chaotic logistics algorithm belongs to a comprehensive analysis method, which integrates training mechanism, classifies a series of problems in skipping training, and realizes comprehensive chaos analysis.…”

Control and Position Tracking for UAVs

“…Intelligent algorithms are mostly bio-inspired heuristic algorithms and those based on machine learning. Heuristic methods include the following algorithms: the A* algorithm [15], evolutionary algorithms (EA) [16], the simulated annealing algorithm (SAA) [17], particle swarm optimization (PSO) [18], the pigeon-inspired optimization algorithm (PIO) [19], the fruit fly optimization algorithm (FOA) [20], the artificial bee colony algorithm (ABC) [21], the salp swarm algorithm (SSA) [22], ant colony optimization (ACO) [23], the grey wolf optimizer (GWO) [24]and the harmony search (HS) [25].…”