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：本文提出了一种基于虎鲸行为的新型群体智能算法以求解问题。该算法称为人工虎鲸算法（AOA），通过模拟虎鲸的生存方式，特别是社会组织结构、回声定位机制及部分狩猎技术。该方法的创新之处在于首次在同一元启发式算法中同步模拟单一物种的多种行为。AOA被适配至离散问题，并应用于四个复杂度等级的迷宫游戏。通过一系列实质性实验为该问题设定算法参数，从成功率、运行时间和解路径长度三个维度评估算法性能。最后，作者基于当前主流的进化算法（包括ACO、BA、BSO、EHO、PSO和WOA）进行了对比实验。综合结果表明，AOA在所有测试算法中表现出明显优势。