Mutation adaptive cuckoo search hybridized naked mole rat algorithm for industrial engineering problems.

Cuckoo Search (CS) is a popular algorithm used to solve numerous challenging problems. In the present work, a novel variant of CS is presented to eliminate its shortcomings. The proposed algorithm is hybridized with the naked mole rat algorithm (NMRA) to enhance the exploitative behavior of CS, and is called Mutated Adaptive Cuckoo Search Algorithm (MaCN). This new algorithm has self-adaptive properties and its key feature is to divide the solutions into multiple sections, which are often called sub-swarms. In addition, a bare-bones search mechanism is also added to enhance exploration. The use of adaptive inertia weights helps optimize the switching probability, an important CS parameter that helps to achieve a balanced operation. The proposed MaCN algorithm is tested on CEC 2005 and CEC 2014 benchmark problems. Comparative studies showed that MaCN delivers promising results in solving CEC competition benchmark problems compared to JADE, success history-based adaptive DE (SHADE), LSHADE-SPACMA and self-adaptive DE (SaDE), among others. In addition to numerical benchmarks, MaCN is used to solve the industrial engineering frame structure and a comparison with hybridization of particle swarm with passive congregation (PSOPC), shuffled frog leaping algorithm hybrid with invasive weed optimization (SFLAIWO), particle swarm ant colony optimization (PSOACO), early strategy with DE (ES-DE), and others show its superiority. In addition, the Wilcoxon rankum and the Freidmann test statistically prove the significance of the proposed MaCN algorithm. MaCN was found to score first rank for the benchmarks. The application of the MaCN algorithm to solve the design problems of the suggests that the best new results are obtained for all test cases.