Symmetry

This study focuses on the single-objective day-ahead economic dispatch of grid-connected microgrids. It proposes an asymmetric GE-RTH-SH framework that integrates gradient-guided elite search (GE), Red-Tailed Hawk Optimization (RTH), and SHADE-lite adaptive differential search (SH). Based on the three-stage search mechanism of RTH (high-altitude circling, low-altitude circling, and dive hunting), GE local fine search and SH adaptive differential search based on successful history are introduced to improve local optimization accuracy and parameter adaptability and alleviate search stagnation. An aggregated microgrid model with a 24 h scheduling cycle and a 1 h time resolution is constructed to coordinate wind power, photovoltaic power, micro gas turbines, fuel cells, energy storage, and power exchange with the grid. The model is implemented in Python, aiming to minimize operation and maintenance costs, fuel costs, and net grid transaction costs, while satisfying power balance, equipment output, ramping, energy storage state of charge (SOC), and grid power exchange constraints. With a budget of 30,000 function evaluations per run, the average cost of 50 independent runs is CNY 5295.06, the standard deviation is 10.01, the best cost is CNY 5269.11, and the average renewable energy supply ratio is 62.55%. Through benchmark-function and ablation experiments, the search effects of the GE and SH modules are analyzed, and the optimization performance, economic performance, and repeatability of the complete algorithm are evaluated through comparisons with other algorithms in the microgrid case and four moderate disturbance scenarios. The results show that the proposed method can provide a feasible optimization scheme for coordinated power allocation and economic operation of grid-connected microgrids.

Soft-error detection in resource-constrained out-of-order processors requires balancing reliability against the cost of redundant execution. This paper proposes an independent dual-trajectory mechanism that preserves separate data-dependence chains while sharing physical backend resources, with error checking at selected architectural-event boundaries. Evaluation on SPECint2006 shows that redundant execution costs 18.3% geometric-mean IPC, versus 5.5% for a SHREC-like checker, along with higher protection-logic area. A 2400-injection campaign achieves an 86.4% conditional detection rate and a 78.5% reduction in adjudicated silent data corruption (SDC) relative to a detection-disabled baseline. A targeted ablation further demonstrates the benefit of independent value propagation, increasing detection from 8.7% to 84.3% compared with shared-source execution. These results quantify the reliability benefits of logical independence and its associated performance and hardware costs within a fixed-capacity out-of-order backend.

Strategic drone-delivery planning couples field locations with drone assignments and customer allocations under field capacity, cumulative payload, and round-trip energy constraints. We formulate this problem as a mixed-integer linear program and derive a Lagrangian primal heuristic with capacity-aware recovery and facility-exchange search. We show that the Lagrangian dual equals the linear-programming relaxation of the original model. For divisible demand, integer drone counts yield an exact reformulation that removes interchangeable drone labels; type-specific allocation flows extend the result to heterogeneous fleets. Experiments on 45 synthetic instances assess feasible solution quality against independent bounds. A same-machine comparison shows that the count formulation solves all 15 instances with 30 customers to optimality, while the Lagrangian heuristic has a mean objective excess of 0.72%. The count formulation also supplies incumbents and bounds for all 60- and 100-customer instances. Resource experiments distinguish changes in optimal planning cost from variation in heuristic solutions: payload tightening leaves the optimum unchanged over the tested ample fleet range, whereas a restrictive battery budget increases cost in a designated random-layout instance. The computational results favor the compact formulation for the tested divisible-demand model and identify the conditions under which the Lagrangian search improves feasible plans.