A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators

Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although AO has significant performance in various problems, like other algorithms, suffers from certain limitations its search mechanism, such as local optima stagnation and convergence speed. This is general problem that faces almost all which can be solved by enhancing process of an optimizer using assistant tool, hybridizing with another or applying techniques boost capability optimizer. Following this concept address critical problem, paper, we present alternative version alleviate shortcomings traditional one. The main idea improved (IAO) use strategy Whale Optimization Algorithm (WOA) AO. Thus, IAO benefits advantages WOA, it avoids well losing solutions diversity through process. Moreover, apply algorithm feature selection technique benchmark functions. More so, tested extensive experimental comparisons WOA several well-known optimizers used techniques, particle swarm (PSO), differential evaluation (DE), mouth flame (MFO), firefly algorithm, genetic (GA). outcomes confirmed operators impact on performance. Thus combined obtained better results compared optimizers.