As industrial systems become more complex, various complexities and uncertainties come into play. Metaheuristic-type optimization techniques have become crucial for effective design, maintenance, and operations of such systems. However, in highly complex industrial systems, conventional metaheuristics are still plagued by various drawbacks. Strategies such as hybridization and algorithmic modifications have been the focus of previous efforts to improve the performance of conventional metaheuristics. This work tackles a large-scale multi-objective (MO) optimization problem: biofuel supply chain. Due to the scale and complexity of the problem, the random matrix approach was employed to modify the stochastic generator segment of the cuckoo search (CS) technique. Comparative analysis was then performed on the computational results produced by the conventional CS technique and the improved CS variants.
Parametric hierarchical matrix approach for the wideband optical response of large-scale molecular aggregates