Forensic-based investigation (FBI) is recently developed metaheuristic algorithm inspired by the suspect investigation–location–pursuit operations of police officers. This study focuses on the search processes of the FBI algorithm, called Step A and Step B, to improve and increase its performance. For this purpose, opposition-based learning is adopted to Step A to enhance diversity, while Cauchy-based mutation is integrated with Step B to guide the search to different regions and to jump out of local minima. To show the effectiveness of these improvements, the proposed algorithm has been tested with two different benchmark sets. To verify the performance of the new modified algorithm, the statistical test is carried out on numerical functions. This study also investigates the application of the proposed algorithm to a set of six real-world problems. The proposed and adapted/integrated methods appear to have a significant impact on the FBI algorithm, which augments its performance, resulting in better solutions than the compared algorithms in most of the functions and real-world problems.

基于法医的调查（FBI）是最近开发的元启发式算法，其灵感来自于警官的嫌疑人调查，定位和追踪操作。这项研究的重点是FBI算法的搜索过程，称为步骤A和步骤B，以改善和提高其性能。为此，在步骤A中采用了基于对立的学习，以增强多样性，而在步骤B中集成了基于柯西的突变，以将搜索引导到不同的区域并跳出局部最小值。为了显示这些改进的有效性，已对所提出的算法进行了两种不同的基准测试。为了验证新算法的性能，对数值函数进行了统计检验。这项研究还研究了提出的算法在六个实际问题中的应用。所提出和改编/集成的方法似乎对FBI算法具有重大影响，从而提高了FBI算法的性能，因此在大多数功能和实际问题上，这些方法都比比较算法更好。