APDO: A Hybrid Aquila Optimizer and Prairie Dog Optimization Metaheuristic Algorithm for Global, Optimization

We propose a hybrid metaheuristic algorithm that combines the strengths of the Aquila Optimizer (AO) and the Prairie Dog Optimization (PDO) algorithms. The proposed algorithm is named the Aquila Prairie Dog Optimization (APDO) algorithm. During the initialization phase of the APDO algorithm, chaotic maps are employed to enhance the exploration capabilities, as they introduce randomness into the initialization process. In addition, opposition-based learning is incorporated during the swarming process, wherein the objective is to consider the opposite values of the current solutions, to expand the diversity of the swarm and to avoid local optimums. Moreover, to assess and evaluate the performance of the APDO algorithm, a comprehensive comparative analysis was conducted by benchmarking it against five widely recognized metaheuristics, across a diverse set of challenging optimization problems, encompassing: unimodal, multimodal, hybrid and composition functions. Finally, the performance evaluation of the APDO algorithm was conducted using the Friedman post hoc Dunn's test, which revealed compelling results indicating that the APDO algorithm demonstrated superior performance in the majority of cases, when compared to the other benchmarked algorithms, thereby showcasing its competitiveness and efficacy in tackling diverse optimization problems.