Meta-Heuristics and Machine Learning Applications in Complex Systems

1.1 Background and Motivation:

Complex systems are present in numerous sectors such as communications, energy systems, manufacturing sector, and others. Such systems are not easier to be understood and optimised because they may exhibit non-linear behaviours, interconnections by numerous components. The main goal of that is to reduce the negative impact of such complications by employing ML and Meta-Heuristic techniques. Meta-heuristics techniques are able to offer robust and flexible optimization methodologies through drawing inspiration from nature processes such as swarm intelligence, evolution, and simulated annealing (Desale et al 2015). They are the best candidates when conventional optimisation techniques are not successful in finding excellent solutions for complex issues (Sood, M & Panchal, V. K. 2020). However, the machine-learning algorithms could be very good at recognizing patterns and predicting outcomes of large datasets. The application of machine learning and meta-heuristic techniques are used in order to develop certain tools for modeling, optimization, and understanding of the complex systems in various disciplines (Memeti et al 2018). With the aim of solving real-world problems for applications ranging from complex systems, academicians have focused their research on the connections between ML and meta-heuristics. These synergies can bring about changes at the level of optimisation algorithms together with systems’ quality and generate new knowledge on complex processes.

1.2 Scope and Objectives:

This survey covers an in-depth analysis of how both machine learning, as well as meta-heuristic techniques are involved in dynamic systems. We propose to encompass holistically the top finds, roadblocks, and probable paths toward identifying a resolution in this cross-disciplinary topic.

Next, we will look into several areas that these techniques can be applied: power systems, complex systems optimisation using hybrid techniques, computer communications, cloud computing, and machining processes demonstrating manifold benefits. Our aim is to present, through the study of this assortment of application areas, the adaptability and the efficiency of ML algorithms associated with meta-heuristics.

Among our goals are to: Describe the main principles of two main groups of machine learning algorithms including meta-heuristic approaches.

Study the use of meta-heuristic techniques for computer communications, cloud computing, power systems and machining processes.

Hybrid methods that blend the machine learning and meta-heuristic optimization techniques into it may be explored by us.

The awareness of the directions that are ahead, the obstacles, and the avenues for further research and advancement in this multidisciplinary discipline.