A Novel Strategy Adaptive Genetic Algorithm with Greedy Local Search for the Permutation Flowshop Scheduling Problem

This article presents a novel Genetic Algorithm with a greedy local search operator that may solve a wide range of sequencing and scheduling discrete optimization problems efficiently. To analyze its performance, we have tested the algorithm on the Permutation Flow-shop Scheduling Problem (PFSSP). Here we present a novel crossover scheme coupled with an innovative mutation scheme that implements local search to facilitate rapid convergence. This novel GA variant provides better results compared to other heuristics, which is apparent from the experimental results and comparisons with other existing algorithms.

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