Mimetic Coral Reefs Optimization (MCRO) has proven highly effective for feature selection due to its capacity to explore diverse solution spaces, enhancing model accuracy and robustness. However, integrating MCRO with local search techniques remains challenging, as it tends to be computationally intensive and prone to premature convergence. To address these issues, this paper introduces a Rank-based Adaptive Brooding (RAB) mechanism, designed to refine the local mimetic search strategy within MCRO. RAB adaptively adjusts the Brooding operator based on the ranks of coral larvae, minimizing disruption to high-rank larvae and harnessing the exploratory potential of lower-rank larvae. This approach promotes a more balanced exploration-exploitation trade-off, leading to faster convergence and enhanced performance in complex problem spaces. The proposed method's efficacy is tested across eight UCI datasets using KNN, Decision Tree, and SVM classifiers, and the results are evaluated by precision, recall, and F1 score. Empirical results reveal that RAB outperforms existing adaptive strategies with fixed Brooding, delivering superior feature selection performance, particularly in high-dimensional datasets. Additionally, the optimization capabilities of RAB were examined using 39 CEC benchmark functions, revealing consistent improvements in feature selection accuracy while demonstrating variable outcomes in broader optimization tasks. Notably, RAB showed significant enhancements in eight benchmark cases, highlighting its potential for broader applicability in optimization scenarios.

In this article, spectral matching of ground motions is presented via the Mouth Brooding Fish (MBF) algorithm that is recently developed. It is based on mouth Brooding fish life cycle. This algorithm utilizes the movements of the mouth Brooding fish and their children’s struggle for survival as a pattern to find the best possible answer. For this purpose, wavelet transform is used to decompose the original ground motions to several levels and then each level is multiplied by a variable. Subsequently, this algorithm is employed to determine the variables and wavelet transform modifies the recorded accelerograms until the response spectrum gets close to a specified design spectrum. The performance of this algorithm is investigated through a numerical example and also it is compared with CBO and ECBO algorithms. The numerical results indicate that the MBF algorithm can to construct very promising results and has merits in solving challenging optimization problems.

Background and Purpose: Rumination is one of the most significant symptoms of several mental illnesses. Rumination is classified into two Brooding and reflection categories. Notwithstanding the prevalence of rumination, the discrimination between its subtypes among various clinical populations has been overlooked. Materials and Methods: In this comparative study, our sample size included 95 patients who were interviewed at a specialized health clinic in Sari City, Iran. For diagnosis, we applied structured clinical interviews, the Yale-Brown obsessive-compulsive scale, and the second version of the Beck depression inventory (BDI). To test our hypothesis, we used the ruminative response scale (RRS) which enabled us to divide ruminations into Brooding and reflection. In the first step, the descriptive statistics of the research, including mean, standard deviation, and standard error of the mean were determined. An independent samples t test was conducted to determine whether people with major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) significantly differed in rumination subtypes. Results: Out of 95 participants, who entered the study, 50 people suffered from MDD, and 45 participants were diagnosed with OCD. For people with MDD, the mean±, SD of the age of the participants was 31. 62±, 6. 94 years, while the age of the people with OCD was 32. 11±, 6. 37. Our independent samples t test analysis determined a significant difference in Brooding between the groups (P<0. 05). Nonetheless, the two groups did not show any difference in reflection. Conclusion: Considering the higher mean for Brooding scores in people with MDD, it is concluded that this population is more likely to adopt this type of passive and maladaptive rumination as a regulatory strategy, which in turn prone depressed people to lower mood.

In this paper, the optimum design of a reinforced concrete one-way ribbed slab, is presented via recently developed metaheuristic algorithm, namely, the Mouth Brooding Fish (MBF). Meta-heuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. The MBF algorithm simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. This algorithm uses the movement, dispersion and protection behavior of Mouth Brooding Fish as a pattern to find the best possible answer. The cost of the system is considered to be the objective function, and the design is based on the American Concrete Institute’ s ACI 318-08 standard. The performance of this algorithm is compared with harmony search (HS), colliding bodies optimization (CBO), particle swarm optimization (PSO), democratic particle swarm optimization (DPSO), charged system search (CSS) and enhanced charged system search (ECSS). The numerical results demonstrate that the MBF algorithm is able to construct very promising results and has merits in solving challenging optimization problems.