A Dynamic Adaptive Firefly Algorithm for Flexible Job Shop Scheduling

An NP-hard problem like Flexible Job Shop Scheduling (FJSP) tends to be more complex and requires computational effort optimize the objectives with contradictory measures. This paper aims address FJSP combined objectives, minimization of make-span, maximum workload, total workload. proposes ‘Hybrid Adaptive Firefly Algorithm’ (HAdFA), a new enhanced version classic Algorithm (FA) embedded adaptive parameters multi concurrently. The proposed algorithm has adopted two strategies, i.e., an randomization parameter (α) effective heterogeneous update rule for fireflies. adaptations by this can help optimization process strike balance between diversification intensification. Further, local search algorithm, Simulated Annealing (SA), is hybridized FA explore solution space efficiently. also attempted solve rarely used integrated approach where assignment sequencing are done simultaneously. Empirical simulations on benchmark instances demonstrate efficacy our algorithms, thus providing competitive edge over other nature-inspired algorithms FJSP.