IWOO: Wave Optics Optimizer Improved with Hybrid Strategies for Numerical Optimization and Engineering Applications
The Wave Optics Optimizer (WOO) incorporates optical diffraction and interference into numerical optimization and has shown competitive performance in benchmarks and engineering applications. However, it can converge slowly, become trapped in local optima, and lose search effectiveness in later iterations. This paper proposes an Improved Wave Optics Optimizer (IWOO) that integrates four complementary strategies. First, a good point set and opposition-based learning are combined in GPS--OBL initialization to improve population quality and diversity. Second, an E-DM nonlinear perturbation mechanism is embedded in temporary triangle-based search to develop the TriEDM multi-path strategy and enhance search capability. Third, Correlation-Guided Groupwise Orthogonal Learning (CGOL) leverages relationships among decision variables to improve search efficiency while reducing computational overhead. Fourth, an adaptive stagnation-aware Runge--Kutta correction is introduced to mitigate premature convergence. IWOO is evaluated on the IEEE CEC2019, CEC2022, and CEC2017 benchmark suites against representative and high-performing optimizers, with ablation studies assessing the contribution of each strategy. Within the representative algorithm group, IWOO achieves Friedman ranks of 1.8000, 1.5833, and 1.4483 on CEC2019, CEC2022, and CEC2017, respectively. Tests on six constrained engineering optimization problems further demonstrate its competitive accuracy, stability, and practical applicability.