Hybrid Optimization Algorithm for Solving Attack-Response Optimization and Engineering Design Problems - Ahmad K. Al Hwaitat; Hussam N. Fakhouri; Jamal Zraqou; Najem Sirhan (Algorithms)
This paper presents JADEDO, a hybrid optimization method that merges the dandelion optimizer’s (DO) dispersal-inspired stages with JADE’s (adaptive differential evolution) dynamic mutation and crossover operators. By integrating these complementary mechanisms, JADEDO effectively balances global exploration and local exploitation for both unimodal and multimodal search spaces. Extensive benchmarking against classical and cutting-edge metaheuristics on the IEEE CEC2022 functions—encompassing unimodal, multimodal, and hybrid landscapes—demonstrates that JADEDO achieves highly competitive results in terms of solution accuracy, convergence speed, and robustness. Statistical analysis using Wilcoxon sum-rank tests further underscores JADEDO’s consistent advantage over several established optimizers, reflecting its proficiency in navigating complex, high-dimensional problems. To validate its real-world applicability, JADEDO was also evaluated on three engineering design problems (pressure vessel, spring, and speed reducer). Notably, it achieved top-tier or near-optimal designs in constrained, high-stakes environments. Moreover, to demonstrate suitability for security-oriented tasks, JADEDO was applied to an attack-response optimization scenario, efficiently identifying cost-effective, low-risk countermeasures under stringent time constraints. These collective findings highlight JADEDO as a robust, flexible, and high-performing framework capable of tackling both benchmark-oriented and practical optimization challenges.