Two Hybrid Variants of Chaotic Honey Badger and Group Teaching Optimization Algorithms in Uncapacitated Facility Location Problem

This paper aims to apply two variants of the hybrid optimization algorithm, namely Chaotic Honey Badger Algorithm (CHBA) and Group Teaching Optimization Algorithm (GTOA) to solve the Uncapacitated Facility Location Problem (UFLP). We enhance using chaotic maps on the HBA to strengthen the search process and hybridization with GTOA leads to a fast exploration process in finding the best solution. There are two variants of hybrid CHBA and GTOA applied. In the first variant, GTOA is applied to determine the initial population of CHBA, then for the second variant, CHBA is used to determine the initial population of GTOA. The performance of these two hybrid variants is used to solve the Uncapacitated Facility Location Problem (UFLP). There were three types of data for UFLP such as small data, medium data, and large data. Furthermore, by comparing the best results it was concluded that hybrid variant GTOA-CHBA has better solution than CHBA-GTOA. The results of the hybrid were compared with other algorithms such as Cuckoo Search (CS), Flower Pollination Algorithm (FPA), and Teaching Learning Based Optimization (TLBO). It was concluded that the average of hybrid GTOA-CHBA better than each algorithm. In terms of best and PRD_min, the hybrid GTOA-CHBA on several data has good results from the other algorithms.