A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

Abstract

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Cuckoo Search Algorithm with Chaotic Maps

“…Some studies have focused on improving LFRW [4][5][6][7][8][9][10] and BSRW [11][12][13][14][15]. Some attempts have been made to combine CS with other optimization techniques like particle swarm optimization [16,17], Tabu search [18], differential evolution [19], ant colony optimization [20], and cooperative coevolutionary framework [21,22]. The above studies have shown their contribution to the research on CS.…”

Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization

“…In recent years, a variety of new nature-inspired metaheuristic algorithms have been reported, e.g., ant lion optimizer (ALO) [19], BA [20], biogeography-based optimization (BBO) [21,22], charged system search (CSS) [23], CS [24][25][26], animal migration optimization (AMO) [27], krill herd (KH) [28][29][30][31][32], wolf search algorithm (WSA) [33], artificial plant optimization algorithm (APOA) [34], human learning optimization (HLO) [35], swarm search [36], earthworm optimization algorithm (EWA) [37], magnetic optimization algorithm (MOA) [38], monarch butterfly optimization (MBO) [39] and others. In fact, MBO is inspired by the migration behavior of the monarch butterflies in nature.…”

An Improved Heuristic Algorithm for UCAV Path Planning

“…Numerous scholars study the path planning problem constantly. The represented techniques of UCAV path planning are like PSO [1,2], dynamic planning [3], * algorithm [4,5], ant colony algorithm [6], genetic algorithm [7,8], and so on [9][10][11][12][13]. Reference [14] discussed sparse algorithm, another effective way, which greatly improves the efficiency of the search, but it is easy to fall into a death cycle under the conditions of lacking maneuver ability.…”