Metaheuristic algorithms are general optimization techniques that demonstrate remarkable performance in solving different classes of optimization problems. However, equipping their stochastic search mechanisms with auxiliary logical strategies can still increase their search capability. Based on this fact, in the current study, the search performance of the Interactive Search Algorithm (ISA), as a metaheuristic search method, is improved by adding a new Bayesian regulator strategy to adjust its search behavior. In this regard, the search patterns of the ISA method are unified and classified according to the memory and learning concepts. Subsequently, during the optimization process, the developed Bayesian module dynamically regulates the ratio of the exploration and exploitation search behaviors by tuning the effect of memory concept. The recent technique is named Bayesian Interactive Search Algorithm (BISA), and its search performance tested on a suite of unconstraint mathematical functions and constrained engineering problems. Acquired outcomes indicate that the proposed BISA considerably speeds up the convergence rate, and improves the stability of the process as well as the accuracy of the solutions, for both engineering and mathematical problems.

元启发式算法是通用的优化技术，在解决不同类别的优化问题时表现出卓越的性能。但是，为其随机搜索机制配备辅助逻辑策略仍可以提高其搜索能力。基于这一事实，在当前的研究中，通过添加新的贝叶斯调节器策略来调整其搜索行为，改进了作为元启发式搜索方法的交互式搜索算法（ISA）的搜索性能。在这方面，ISA方法的搜索模式根据记忆和学习概念被统一和分类。随后，在优化过程中，开发的贝叶斯模块通过调整内存概念的效果来动态调节探索和开发搜索行为的比率。最近的这项技术被称为贝叶斯交互式搜索算法（BISA），其搜索性能在一系列无约束的数学函数和受约束的工程问题上进行了测试。获得的结果表明，针对工程和数学问题，拟议的BISA大大加快了收敛速度，并提高了过程的稳定性以及解决方案的准确性。