Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using a Clustering-Genetic-Algorithm-Based Approach

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current only provide a single optimal schedule. Therefore, new algorithm is proposed, by combining k-means clustering and Genetic Algorithm (GA), multimodal PFSSP. In algorithm, first utilized to cluster individuals every generation into different clusters, based on some machine-sequence-related features. Next, operators GA are applied belonging same find multiple global optima. Unlike standard GA, where all belong cluster, in approach, these split clusters crossover operator restricted cluster. Doing so, enabled potentially optima each The performance was evaluated its application benchmark results obtained also compared when other niching such as clearing method, sharing fitness, hybrid approach fitness used. case studies showed that able consistently converge better solutions than three algorithms.