Improved Multiverse Optimization Algorithm for Fuzzy Flexible Job-Shop Scheduling Problem

An improved multiverse optimization (IMVO) algorithm is proposed herein for the fuzzy flexible job-shop scheduling problem pertaining to non-deterministic polynomial-time hard (NP-hard) problem. First, we designed a hybrid initialization method improve quality of initial solution, and thereafter, introduced self-crossing technique along with an insert-based heuristic simulate process exchanging objects between black/white holes wormholes, respectively. Second, universe selection mechanism reduce possibility falling into local optimum. Third, four kinds neighborhood structures were search ability algorithm. In decoding operation, adopted shift-left strategy completely utilize idle time machine. Finally, numerous experiments conducted on three benchmark test sets various types sizes investigate performance IMVO The experimental results demonstrated effectiveness algorithm, especially in large-scale instances, displaying strong superiority average maximum enhancement efficiency 50.44%.