Ameliorated Golden jackal optimization (AGJO) with enhanced movement and multi-angle position updating strategy for solving engineering problems

Golden jackal optimization (GJO), a lately published meta-heuristic optimization algorithm, is inspired by the foraging behavior of pairs of golden jackals and shows an acceptable optimization performance. However, GJO exists a shortage in balancing exploration and exploitation, as it completely focuses on exploitation in the later iterations. A new variant of GJO, named Ameliorated Golden jackal optimization (AGJO), is proposed in this study. Three strategies are employed in AGJO to alleviate the imbalance between the exploration and exploitation of GJO: the enhanced movement strategy, the global search strategy, and the multi-angle position update strategy for prey. An environmental disturbance factor is added to the third strategy to strengthen GJO's ability to evade the local optimal solution. The performance of AGJO is tested and compared with GJO and seven well-known meta-heuristic algorithms for 23 classical benchmark functions, CEC 2017 and the first ten functions of CEC 2006 with constaints. These results show that AGJO performs better over 90% of these functions compared to GJO. Also, AGJO is also highly competitive in terms of optimization capability and convergence speed compared with other algorithms. Finally, AGJO is applied to optimize engineering problems, including five classical engineering design problems with constraints and a displacement prediction problem of composite pipes. Results show that AGJO is a potential algorithm for solving these real problems rather than GJO and other algorithms.