Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Golden jackal optimization (GJO) is inspired by the cooperative attacking behavior of golden jackals and mainly simulates searching for prey, stalking enclosing pouncing on prey to solve complicated problems. However, basic GJO has disadvantages premature convergence, a slow convergence rate low computation precision. To enhance overall search abilities, an enhanced (EGJO) method with elite opposition-based learning technique simplex proposed address adaptive infinite impulse response system identification. The intention minimize error fitness value obtain appropriate control parameters. boosts population diversity, enhances exploration ability, extends range avoids stagnation. accelerates process, exploitation improves computational precision increases depth. EGJO can not only achieve complementary advantages avoid stagnation but also balance arrive at best value. Three sets experiments are used verify effectiveness feasibility EGJO. experimental results clearly demonstrate that efficiency recognition accuracy superior those AOA, GTO, HHO, MDWA, RSO, WOA, TSA GJO. faster rate, higher precision, better parameters value, it stable resilient in solving IIR identification problem.