Solving many-objective optimization problems using selection hyper-heuristics

To effectively solve discrete optimization problems, meta-heuristics and heuristics have been used but their performance suffers drastically in the cross-domain applications. Hence, hyper-heuristics (HHs) have been used to cater to cross-domain problems. In literature, different HHs and meta-heuristics have been applied to solve the Many-objective Job-Shop Scheduling problem (MaOJSSP) and Many-objective Knapsack problem (MaOKSP) but the results are not convincing. Furthermore, no researchers have tried to solve these problems as cross-domain together using HHs. Additionally, the considered HH known as the cricket-based selection hyper-heuristic (CB-SHH) has not applied to any variation of the Job-shop scheduling problem (JSP) and the knapsack problem (KSP). This paper compares the performance of recently proposed HHs named CB-SHH, H-ACO, MARP-NSGAIII, and meta-heuristics named MPMOGA, MOEA/D on MaOKSP, MaOJSSP and benchmark problems. The performance of state-of-the-art HHs and meta-heuristics have been compared using hypervolume (HV) and µ norm. The main contribution of the paper is to effectively solve the MaOJSSP and MaOKSP using HHs and to prove the effectiveness of the best HHs on benchmark problems. It is proven through experiments that the CB-SHH is the best-performing algorithm on 44 out of 48 instances across all datasets and is the best cross-domain algorithm across the datasets.