Parameter less hybrid IG-Jaya approach for permutation flow shop scheduling problem

Permutation flow shop scheduling problem (PFSP) is a well-known NP-hard with extensive engineering relevance. Consequently, various meta-heuristics have been proposed to obtain near optimum solutions. However, most of them involve tuning algorithm-specific parameters, which leads excessive computational complexities. A recently developed meta-heuristic named Jaya algorithm simple yet efficient as it parameter-less algorithm, thus does not require parameters. the size grows, loose solution diversity and tends get trapped at local optima. To alleviate such drawbacks while retaining feature Jaya, present paper proposes Hybrid Iterated Greedy based (HIGJ). The approach combines novel integration Jaya's population-based optimization single solution-based iterated greedy for enhancing population members retrieve improved objective minimize makespan. destruction construction phase (IG) embedded into best algorithm. Furthermore, search method incorporated improve overall quality candidate solutions resulting in faster convergence towards optimal solution. An exhaustive comparative study along statistical analysis conducted public benchmarks realize effectiveness HIGJ Computational results reveal that yields outperforms some available literature.