连续和分化问题三阶段人工猎物等级 (A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems)

In this paper, a new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The algorithm called artificial orca algorithm (AOA) consists of simulating the orca lifestyle and in particular the social organization, the echolocation mechanism, and some hunting techniques. The originality of the proposal is that for the first time a meta-heuristic simulates simultaneously several behaviors of just one animal species. AOA was adapted to discrete problems and applied on the maze game with four level of complexity. A bunch of substantial experiments were undertaken to set the algorithm parameters for this issue. The algorithm performance was assessed by considering the success rate, the run time, and the solution path size. Finally, for comparison purposes, the authors conducted a set of experiments on state-of-the-art evolutionary algorithms, namely ACO, BA, BSO, EHO, PSO, and WOA. The overall obtained results clearly show the superiority of AOA over the other tested algorithms.

翻译：在本文中,提出了一种基于恒星行为的新的群状智能算法,用于解决问题。称为人工或成形算法(AOA)的算法包括模拟恒星生活方式,特别是社会组织、回声定位机制和某些狩猎技术。提案的原创性是,首次将一个动物物种的元湿性模拟同时同时同时进行若干种行为。AOA适应于不同问题,并应用于四级复杂的迷宫游戏。为了确定这一问题的算法参数,进行了大量的实验。算法性能是通过考虑成功率、运行时间和解决方案路径大小来评估的。最后,为比较起见,作者们对最新进化算法进行了一系列实验,即ACO、BA、BSO、EHO、PSO和WA。获得的总体结果清楚地表明了AOA优于其他测试的算法。