Abstract
Massive data sets and complex scheduling processes have high-dimensional and non-convex features bringing challenges on various applications. With deep insight into the bio-heuristic opinion, we propose a novel Beetle Colony Optimization (BCO) being able to adapt NP-hard issues to meet growing application demands. Two important mechanisms are introduced into the proposed BCO algorithm. The first one is Beetle Antennae Search (BAS), which is a mechanism of random search along the gradient direction but not use gradient information at all. The second one is swarm intelligence, which is a collective mechanism of decentralized and self-organized agents. Both of them have reached a performance balance to elevate the proposed algorithm to maintain a wide search horizon and high search efficiency. Finally, our algorithm is applied to traveling salesman problem, and quadratic assignment problem and possesses excellent performance, which also shows that the algorithm has good applicability from the side. The effectiveness of the algorithm is also substantiated by comparing the results with the original ant colony optimization (ACO) algorithm in 3D simulation model experimental path planning.
Highlights
In recent years, unmanned aerial vehicles (UAV) have developed rapidly and the market has been expanding
Wu et al proposed a novel path palnner named obstacle avoidance beetle antennae search (OABAS) algorithm applied to the global path planning of unmanned aerial vehicles (UAVs) [22]
Two important mechanisms are introduced into Beetle Colony Optimization (BCO) algorithm
Summary
In recent years, unmanned aerial vehicles (UAV) have developed rapidly and the market has been expanding. Bio-heuristic algorithms include simulated annealing algorithm [16], genetic algorithm (GA) [17], particle swarm optimization algorithm (PSO) [18], ant colony optimization (ACO) [19] algorithm, artificial fish swarm algorithm (AFSA) [20] and so on These heuristic algorithms are generated from the characteristics of biological creatures in nature, such as biological foraging behavior, group behavior,biological evolution,which had been used widely for different applications [21]. Wu et al proposed a novel path palnner named obstacle avoidance beetle antennae search (OABAS) algorithm applied to the global path planning of unmanned aerial vehicles (UAVs) [22]. (c) A novel heuristic optimization algorithm, termed beetle colony optimization algorithm (BCO), is proposed and it can solve traveling salesman problem(TSP), quadratic assignment problem(QAP) and be applied to path planning for UAV in 3D space.
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