A range of complicated real-world problems have inspired the development of several optimization methods. Here, a novel hybrid version of the Ant colony optimization (ACO) method is developed using the sample space reduction technique of the Cohort Intelligence (CI) Algorithm. The algorithm is developed, and accuracy is tested by solving 35 standard benchmark test functions. Furthermore, the constrained version of the algorithm is used to solve two mechanical design problems involving stepped cantilever beams and I-section beams. The effectiveness of the proposed technique of solution is evaluated relative to contemporary algorithmic approaches that are already in use. The results show that our proposed hybrid ACO-CI algorithm will take lesser number of iterations to produce the desired output which means lesser computational time. For the minimization of weight of stepped cantilever beam and deflection in I-section beam a proposed hybrid ACO-CI algorithm yielded best results when compared to other existing algorithms. The proposed work could be investigate for variegated real world applications encompassing domains of engineering, combinatorial and health care problems.

翻译：一系列复杂的现实世界问题催生了多种优化方法的发展。本文基于群体智能（CI）算法的样本空间缩减技术，提出了一种新颖的蚁群优化（ACO）混合版本。该算法被开发并通过对35个标准基准测试函数的求解验证了其准确性。此外，采用该算法的约束版本解决了涉及阶梯悬臂梁和工字梁的两个机械设计问题。通过对比当前已应用的算法方法，评估了所提求解技术的有效性。结果表明，本文提出的混合ACO-CI算法能以更少的迭代次数产生期望输出，即降低计算时间。在阶梯悬臂梁的重量最小化与工字梁的挠度最小化问题中，与其他现有算法相比，所提出的混合ACO-CI算法取得了最优结果。该研究工作可进一步拓展至工程、组合优化及医疗保健等领域的多样化现实应用。