Actor Optimization Algorithm: A Novel Approach for Engineering Design Challenges

In this paper, a novel human-based metaheuristic algorithm called Actor Optimization Algorithm (AOA) is introduced. AOA mimics the behaviors of an actor when playing a role. The main idea in designing AOA is derived from a specific behavior of the actor including (i) simulating the movements and dialogues of the given role and (ii) practicing to better present the assigned role. The theory of AOA is stated and mathematically modeled in the phases of exploration and exploitation. The performance of AOA to address real-world applications is evaluated on the CEC 2011 test suite. The optimization results show that AOA, with its high ability in exploration, exploitation, and balancing during the search process, achieved suitable results. In addition, the performance of AOA was challenged by comparing it with 12 known metaheuristic algorithms. Result comparison showed that the proposed AOA outperformed the competing algorithms by 100% (in all 22 optimization problems) of the CEC 2011 test suite. The simulation results show that AOA has a successful performance in handling optimization tasks in real-world applications by achieving better results in competition with the compared algorithms.