This paper proposes a novel population-based metaheuristic optimization algorithm, called Adolescent Identity Search Algorithm (AISA), which is inspired by the process of identity development/search of adolescents. AISA simulates the identity formation behavior of adolescents in the peer group. This behavior is modeled mathematically to solve optimization problems. The proposed algorithm is evaluated on thirty-nine well-known unimodal, multimodal, fixed-dimensional multimodal, composite and CEC 2019 benchmark functions to test exploration, exploitation, local optima avoidance, and convergence properties. The results are verified by an extensive comparative study with thirteen state-of-art metaheuristic algorithms. Furthermore, AISA has been used to solve IIR system identification and inverse kinematics problem of a seven Degrees Of Freedom (7-DOF) robot manipulator considered as the real-life engineering applications. The overall optimization results demonstrate that AISA possesses a strong and robust capability to produce superior performance over other competitor metaheuristic algorithms in solving various complex numerical optimization problems.

本文提出了一种新颖的基于群体的元启发式优化算法，称为青少年身份搜索算法（AISA），该算法受青少年身份发展/搜索过程的启发。AISA模拟同龄人群体中青少年的身份形成行为。对这种行为进行数学建模以解决优化问题。在39个著名的单峰，多峰，固定维多峰，复合和CEC 2019基准函数上对提出的算法进行了评估，以测试勘探，开发，避免局部最优性和收敛性。通过使用十三种最新的元启发式算法进行的广泛比较研究，验证了结果。此外，AISA已用于解决被视为现实工程应用的七自由度（7-DOF）机器人操纵器的IIR系统识别和逆运动学问题。总体优化结果表明，在解决各种复杂的数值优化问题时，AISA具有比其他竞争对手元启发式算法更高的性能。