Metaheuristic algorithms and their applications in performance optimization of cyber-physical systems having applications in logistics

In the industrial revolution 4.0 the applicability of cyber physical systems (CPS) extensively increased in various sectors including logistics, health, and manufacturing. The integration of CPS in logistics has revolutionized the way goods are transported, managed, and monitored. CPS combines digital and physical components to enhance the efficiency and effectiveness of logistics operations. However, to ensure the reliable and continuous operation of CPS, optimizing the availability of its subsystems is paramount. Hence, in present study, an effort is made to explore the applicability of metaheuristic approaches in the performance evaluation of cyber physical systems and a comprehensive evaluation framework is also proposed to compare the performance of metaheuristic algorithms. For this purpose, a Markov model of cyber physical system developed by considering constant failure and repair rates for all components. The failure and repair rates are followed exponential distribution. The concept of cold standby redundancy is utilized for two components including analog components and sensors & actuators unit. All components of cyber physical system can face failure during the working process and are perfectly repairable. The mathematical expression of system is derived and optimized by using metaheuristic approaches namely grey wolf optimization (GWO), cuckoo search algorithm (CS), dragonfly optimization (DA), grasshopper optimization algorithm (GOA) and cat swarm optimization (CSO). The numerical expression of the system availability obtained at various population sizes and estimated parameters derived. It is revealed from the numerical investigation that GWO and CSO outperform all optimization techniques. GWO attains its maximum availability 0.9983290 at population size 140 over 500 iterations and CSO attains its maximum availability 0.9983297 at population size 120 over 1000 iterations. The results of present study provide valuable insights into the strengths and weaknesses of each metaheuristic approach in the context of CPS subsystem optimization. This comparative analysis can guide logistics professionals and researchers in selecting the most suitable optimization algorithm for their specific CPS applications.