A Butterfly Optimization Algorithm Enhanced by Dance-Based Healing Strategies for Global Optimization and Real-World Engineering Applications

Microgrid scheduling is a challenging optimization problem because renewable energy generation, energy storage behavior, load demand, and grid interaction must be coordinated under nonlinear and constrained operating conditions. To improve the search performance of the original Butterfly Optimization Algorithm (BOA), this paper proposes an Improved Butterfly Optimization Algorithm (IBOA) for global optimization and microgrid scheduling. Three strategies are embedded into the BOA framework. First, the Dance Synchronization Guidance Strategy uses both the current global best solution and the dominant-group center to reduce excessive dependence on a single leader and improve population cooperation. Second, the Dance Emotion Disturbance Strategy introduces an adaptive perturbation term into the local search process, which helps the algorithm escape stagnant regions. Third, the Exponential Fragrance Decay Strategy dynamically adjusts the sensory modality parameter, allowing the search process to gradually shift from global exploration to local refinement. The performance of IBOA is evaluated through the IEEE CEC2017 and CEC2022 benchmark suites under different dimensions. The Friedman ranking results show that IBOA achieves the best mean ranks on CEC2017 with values of 1.13, 1.23, and 1.87 for 30-, 50-, and 100-dimensional cases, respectively. On CEC2022, IBOA also ranks first, with mean ranks of 1.50 and 1.00 for 10- and 20-dimensional cases. In the microgrid scheduling case, IBOA obtains the lowest average operating cost of 1443.56 with a standard deviation of 69.61 over 30 independent runs. Compared with CCO, CBSO, and GWCA, the average cost is reduced by approximately 13.58%, 14.98%, and 15.39%, respectively. Moreover, compared with the original BOA, the average cost is reduced from 28,338.69 to 1443.56. These results indicate that IBOA provides a more stable and cost-effective optimization approach for both benchmark optimization and microgrid scheduling problems.