The field of metaheuristics and optimization is witnessing significant developments, with a focus on improving the performance and credibility of evaluations. Researchers are working on enhancing existing algorithms, such as the RIME and educational competition optimizer, by introducing new strategies like covariance learning and diversity enhancement. Additionally, there is a growing emphasis on considering the computational cost and resource constraints in evaluating the effectiveness of AI systems. Novel approaches, such as the use of anytime performance curves and expected running time metrics, are being proposed to provide more accurate and practically relevant assessments. Furthermore, advancements in linear programming and interpolation problems are also being made, with new methods and techniques being developed to improve efficiency and solvability. Noteworthy papers include: Time-Fair Benchmarking for Metaheuristics, which introduces a restart-fair protocol for fixed-time comparisons, and Efficient Compilation of Algorithms into Compact Linear Programs, which proposes a novel approach for generating substantially smaller LPs from algorithms. SWE-Effi is also notable for re-evaluating software AI agent system effectiveness under resource constraints.