A modified LAB algorithm is introduced in this paper. It builds upon the original LAB algorithm (Reddy et al. 2023), which is a socio-inspired algorithm that models competitive and learning behaviours within a group, establishing hierarchical roles. The proposed algorithm incorporates the roulette wheel approach and a reduction factor introducing inter-group competition and iteratively narrowing down the sample space. The algorithm is validated by solving the benchmark test problems from CEC 2005 and CEC 2017. The solutions are validated using standard statistical tests such as two-sided and pairwise signed rank Wilcoxon test and Friedman rank test. The algorithm exhibited improved and superior robustness as well as search space exploration capabilities. Furthermore, a Clustering-Based Search Space Reduction (C-SSR) method is proposed, making the algorithm capable to solve constrained problems. The C-SSR method enables the algorithm to identify clusters of feasible regions, satisfying the constraints and contributing to achieve the optimal solution. This method demonstrates its effectiveness as a potential alternative to traditional constraint handling techniques. The results obtained using the Modified LAB algorithm are then compared with those achieved by other recent metaheuristic algorithms.

介绍了一种修改后的LAB算法，该算法建立在原始LAB算法的基础上，通过引入轮盘赌方法和缩减系数，引入了群体间竞争并逐步缩小样本空间。该算法通过解决CEC 2005和CEC 2017的基准测试问题来验证，并使用双边和成对符号秩Wilcoxon检验以及Friedman秩检验对解决方案进行验证。该算法表现出更强的稳健性和搜索空间探索能力。此外，还提出了一种基于聚类的搜索空间缩减方法，使该算法能够解决约束问题。这种方法能够识别满足约束条件并有助于实现最优解的可行区域聚类，展示了作为传统约束处理技术潜在替代方案的有效性。最后，将使用修改后的LAB算法所获得的结果与其他最新的元启发式算法进行比较。

基于聚类的搜索空间缩减方法的改进LAB算法用于解决工程设计问题