Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

The Mixed No-Idle Flow-shop Scheduling Problem (MNIFSP) is an extension of flow-shop scheduling, which has practical significance and application prospects in production scheduling. To improve the efficacy solving complicated multiobjective MNIFSP, a MultiDirection Update (MDU) based Multiobjective Particle Swarm Optimization (MDU-MoPSO) proposed this study. For biobjective optimization problem MNIFSP with minimization makespan total processing time, MDU strategy divides particles into three subgroups according to hybrid selection mechanism. Each subgroup prefers one convergence direction. Two are individually close two edge areas Pareto Front (PF) serve objectives, whereas other approaches central area PF, preferring objectives at same time. MDU-MoPSO adopts job sequence representation method exchange sequence-based particle update operation, can better reflect characteristics differences among particles. updates multiple directions interacts each direction, speeds up while maintaining good distribution performance. experimental results comparison six classical evolutionary algorithms for various benchmark problems demonstrate effectiveness algorithm.