BioCoCo-MTO for Constrained Multi-Objective Smart Grid Dispatch in Smart Electrical Systems

Smart electrical systems face complex optimization challenges involving multiple conflicting objectives and stringent operational constraints across dynamic scenarios. Existing methods, such as Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Evolutionary Algorithm (MOEA)/D-DE, handle each scenario independently, leading to inefficient knowledge reuse, whereas Multifactorial Evolutionary Algorithm II (MFEA-II) suffers from adverse transfer under heterogeneous constraints. To address these limitations, this research proposes Bio-inspired Collaborative-Competitive Multi-Tasking Optimizer (BioCoCo-MTO). This model correlated operational scenarios’ peak load, off-peak, and renewable-uncertainty as linked optimization tasks within a unified framework. A constraint-aware selective knowledge transfer mechanism enables beneficial cross-task information sharing while suppressing negative transfer. A symbiotic-competitive evolutionary strategy adaptively balances co-operation and competition among tasks based on task feasibility and constraint-violation behavior. Experiments on the IEEE Reliability Test System Grid Modernization Lab Consortium (RTS-GMLC) benchmark demonstrate that BioCoCo-MTO achieves a mean feasibility rate of 97.6%, a mean hypervolume of 0.9035, and reduces convergence generations by 37.6% over NSGA-III, consistently outperforming NSGA-III, Multi-Objective Evolutionary Algorithm based on Decomposition with Differential Evolution (MOEA/D-DE), and MFEA-II across all performance metrics, confirming its effectiveness for constrained multi-objective optimization in smart electrical systems.