An Enhanced Crayfish Optimization Algorithm Inspired by Cross-Media Performing Arts Strategies for Global Optimization and Real-World Engineering Applications

Microgrid scheduling is a nonlinear, multimodal, and highly constrained optimization problem that requires effective coordination among renewable generation, controllable distributed generators, energy storage, and grid interaction. To improve the search capability of the original Crayfish Optimization Algorithm (COA), this study proposes an Enhanced Crayfish Optimization Algorithm (ECOA) inspired by the coordination characteristics of cross-media performing arts. Rather than simply combining independent operators, ECOA constructs a state-aware and behavior-specific adaptive search framework around the intrinsic behavioral structure of COA. Specifically, a rhythm-guided adaptive search strategy is embedded into the summer-resort behavior to dynamically regulate search intensity, a multi-role collaborative guidance strategy reconstructs the competition behavior through time-varying information interaction, and a stagnation-aware stage-transition perturbation strategy selectively reactivates exploration when the search becomes stagnant. Extensive experiments are conducted on the CEC2014, CEC2017, and CEC2022 benchmark suites under six-dimensional settings, and the results are compared with those of nine representative metaheuristic algorithms. The results show that ECOA achieves highly competitive convergence accuracy and run-to-run stability on most benchmark functions. Wilcoxon rank-sum tests and Cliff’s delta effect-size analysis indicate that many of the observed improvements are statistically significant and practically meaningful, while the Friedman test ranks ECOA first in all six benchmark settings. Ablation experiments further demonstrate that all three mechanisms contribute positively to performance and that their coordinated integration is more effective than any individual strategy. Parameter-sensitivity analysis shows that ECOA maintains effective convergence over a relatively broad range of the disturbance coefficient λ, with λ = 0.5 providing a balanced setting across different landscapes. Finally, ECOA is applied to a deterministic day-ahead microgrid scheduling problem with explicit feasibility repair and power-balance penalty handling. Under the considered operating scenario, ECOA achieves the lowest mean operating cost of 1361.8028 among all compared algorithms and produces coordinated power-allocation schedules satisfying the modeled operational constraints. These results demonstrate the effectiveness of ECOA for complex numerical optimization and constrained engineering scheduling problems.