When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed.

With the increasing size of datasets in data mining applications, feature selection has become critical for enhancing classification accuracy and reducing computational complexity. In this study, a novel binary feature selection algorithm, called bWSO-log, is proposed based on the White Shark Optimizer (WSO). Unlike the commonly used S-shaped and V-shaped transfer functions in the literature, the WSO algorithm is converted into a binary form for the first time using a logarithmic transfer function. The performance of the proposed method was tested on nineteen benchmark datasets and compared with eight widely used metaheuristic algorithms. The results show that the bWSO-log algorithm demonstrates superior or competitive performance in terms of classification accuracy and the number of selected features. These findings reveal the effectiveness of the proposed logarithmic function and highlight the potential of WSO-based binary optimization in feature selection problems.

Metaheuristic feature selection is frequently used to improve distance-based classification on tabular datasets, yet the empirical value of adding chaotic dynamics to already enhanced optimizers remains unclear. This paper presents a controlled multi-dataset benchmark of a binary Whale Optimization Algorithm (WOA) feature selector combined with Lévy-flight refinement and a k-nearest neighbors (k-NN) classifier. Four fixed chaotic-map pairs, namely Logistic+Cubic, Sine+Sinusoidal, Singer+Circle and Iterative+Tent, are then applied to the same selected feature subspace and evaluated against the WOA+Lévy + k-NN baseline. The experiments use eight publicly available tabular datasets covering diagnostic, voting, signal-processing, combinatorial and chemical classification problems. The evaluation uses repeated stratified cross-validation, train-fold-only min-max scaling, a fixed k = 3 classifier, and non-parametric statistical testing with Friedman, Wilcoxon signed-rank and Holm-Bonferroni procedures. The results do not support a general claim that chaotic-map combinations improve the baseline. On the contrary, the WOA+Lévy + k-NN baseline obtains the best average rank for both accuracy and macro-F1. The Friedman test indicates a statistically detectable difference among methods for macro-F1, but the rank pattern favors the baseline, and no chaotic-map pair significantly outperforms it after Holm correction. Dataset-level analysis shows that chaotic transformations can occasionally help, as in Tic-tac-toe, but they more often degrade or leave performance unchanged, especially on Breast-cancer-wisconsin, Ionosphere, Sonar, WDBC and Wine. The contribution is therefore a reproducible boundary-condition analysis rather than a claim of a universally superior optimizer. Although the benchmark includes a raw k-NN control, a standard WOA ablation, and an EvoMapX-inspired population evolution analysis, its conclusions remain limited to the evaluated post-selection integration strategy and the four representative fixed chaotic-map pairs rather than all possible chaotic optimization designs.