Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces $MsMA$, a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, $MsMA$ divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques.

Fair comparison with state-of-the-art evolutionary algorithms is crucial, but is obstructed by differences in problems, parameters, and stopping criteria across studies. Metaheuristic frameworks can help, but often lack clarity on algorithm versions, improvements, or deviations. Some also restrict parameter configuration. We analysed source codes and identified inconsistencies between implementations. Performance comparisons across frameworks, even with identical settings, revealed significant differences, sometimes even with the authors’ own code. This questions the validity of comparisons using such frameworks. We provide guidelines to improve open-source metaheuristics, aiming to support more credible and reliable comparative studies.

This paper presents a grammarware-based approach to developing active video games (AVGs) for sensor-driven training systems. The GCGame domain-specific language (DSL) is introduced to define game logic, sensor interactions, and timing behavior formally. This approach ensures cross-platform consistency, supports real-time configurability, and simplifies the integration of optimization and visualization tools. The presented system, called GCBLE, serves as a case study, demonstrating how grammarware enhances modularity, maintainability, and adaptability in real-world physical interaction applications. The results highlight the potential of a DSL-driven design to bridge the gap between developers and domain experts in embedded interactive systems.

Teaching programming presents numerous challenges, one of which is selecting the most effective notation to introduce programming concepts to beginners. This study explores different notation approaches for learning fundamental programming concepts, with the objective of assessing how notation choice influences beginners’ performance. To investigate this, we conducted a controlled experiment during short-term visits aimed at promoting programming in primary schools. Our multinational study divided participants into three groups: one using blockbased notation (Poligot), one using text-based notation (Python), and one using both simultaneously (Poliglot). After completing a training session, the participants engaged in practical programming tasks to assess their performance in Python or the multiple-representation environment Poliglot. The results indicate that the choice of notation did not impact the performance of the participants significantly, as no statistically significant differences were found between the three groups. These findings were consistent across two different countries. Our study suggests that educators can use different notations (Python, Poliglot, or a combination) confidently when introducing programming to beginners. However, the performance results may improve when training sessions are extended over a longer period.

This preliminary study investigates how scientific paper components influence end-users' metaheuristic algorithms (MAs) implementation. Focusing on two papers presenting the Monkey King Evolution (MKE) and Sine Cosine Algorithm (SCA), we examined a sample of 22 Computer Science engineers tasked with implementing these MAs based on the papers and supplementary resources. Through questionnaires and statistical analysis, we assessed the impact of pseudo-code, Figures, Tables, and overall presentation on comprehension and implementation success. The findings indicate that coherent pseudo-code and visual aids, such as Figures illustrating solution movements, are critical for end-users, while inaccuracies or complex descriptions hinder implementation. The SCA paper, rated significantly higher than the MKE paper, facilitated faster and more accurate implementations. Despite access to the MATLAB source code, challenges persisted in adapting the implementations to Java, highlighting the need for platform-agnostic resources. We propose guidelines for MA papers, including detailed pseudo-code, diverse implementations, and additional implementation instructions. Although limited by a small sample, these insights justify further research into improving scientific papers for practical MA adoption.