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PARALLEL EVOLUTIONARY ALGORITHMS FOR UAV PATH PLANNING

Evolutionary computation (EC) techniques have been successfully applied to compute near-optimal paths for unmanned aerial vehicles (UAVs). Premature convergence prevents evolutionary-based algorithms from reaching global optimal solutions. This often leads to unsatisfactory routes that are suboptimal to optimal path planning problems. To overcome this problem, this paper presents a framework of parallel evolutionary algorithms for UAV path planning, in which several populations evolve simultaneously and compete with each other. The parallel evolution technique provides more exploration capability to planners and significantly reduces the probability that planners are trapped in local optimal solutions.