Communication technology's advancement has revolutionized industries and shaped social norms by changing how we connect and communicate. Every technological development, from the earliest days of telegraphy to the present day of wireless connectivity and high-speed internet, has brought the world closer together. Global networks have made it easier to interchange data, which has improved commercial operations and changed human interactions by erasing the need for physical boundaries. The need for reliable and efficient computer communications has grown in the digital age at present. The complicated optimization problems that arise in network design, routing protocols, resource allocation, and quality of service management can now be solved successfully with the help of metaheuristic algorithms. Such algorithms like Simulated Annealing (SA), Ant Colony Optimization (ACO), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO).
Metaheuristic and Machine Learning Optimization Strategies for Complex Systems
TopBy providing flexible and adaptive approaches to problem-solving, metaheuristic algorithms constitute a class of optimization techniques that go beyond the limitations of conventional optimization methods. These algorithms are especially well-suited for difficult, large-scale optimization problems where obtaining an ideal solution is a difficult task because of their ability to effectively explore and exploit solution areas. In contrast to traditional algorithms that follow particular issue structures, metaheuristics offer a higher-level framework that may be explicitly tailored to different problem kinds (Plagianakos, 2008). The capacity of metaheuristics to iteratively explore the solution space and progressively improve and refine solutions over a number of iterations is one of their distinguishing characteristics. These algorithms develop novel and adaptive optimization techniques by taking cues from social behaviour, natural processes, and different problem-solving techniques.
Metaheuristic algorithms that are frequently used include, but are not limited to, Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Tabu Search (TS). A distinct set of ideas and methods are employed by each of these algorithms to facilitate the optimization process.
Genetic algorithms (GAs) mimic the evolutionary process to evolve a population of possible solutions over several generations, favouring those that have desirable qualities. GAs is inspired by natural selection and genetics. Using a controlled random walk across the solution space, Simulated Annealing (SA) allows the algorithm to accept poor answers at first and progressively converge towards the global optimum. SA is modelled after the annealing process in metallurgy (Soma et al., 2020). PSO is a population-level strategy that moves particles through the solution space, adapting their placements based on both individual and collective experiences. It is inspired by the social behaviour of fish and birds. An algorithm known as Ant Colony Optimization (ACO) uses pheromone-based communication to direct the search process and accumulates pheromone trails to lead the algorithm towards optimal solutions. The system is inspired by the foraging activity of ants. Tabu Search (TS): TS maintains a “tabu” list to prevent revisiting recently investigated solutions, hence encouraging variety and intensification during the search. This technique mimics the memory and exploration mechanisms of human problem-solving.
Metaheuristic algorithms are effective when they can adapt to different problem landscapes, strike a balance between exploration and exploitation, and converge to near-optimal solutions in reasonable amounts of time. Their versatility renders them invaluable instruments for tackling practical optimization problems in many fields like as engineering, finance, logistics, and artificial intelligence. Metaheuristic algorithms are at the forefront of modern optimization approaches due to their versatility and efficiency as the landscape of optimization issues continues to change.
The characteristics of meta heuristic algorithms are Global Optimization, Adaptability, Exploration and Exploitation, Noisy or Incomplete Information, Parallelization is explained in Figure 1.
Figure 1. Characteristics of metaheuristic algorithms
Global Optimization: By examining many areas of the solution space, metaheuristic algorithms seek to achieve global optimization. When dealing with complicated problems that have several optima or uneven landscapes, this is essential (Agrawal, Hattan, Abutarboush et al, 2021).
Adaptability: One of metaheuristics' main advantages is their capacity for adaptation. During the optimization process, these algorithms can dynamically modify their tactics in response to the changing characteristics of the current problem.
Exploration and Exploitation: Metaheuristics aim to achieve a balance between the two processes of exploration and exploitation, or intensification and diversification, respectively. They use existing good solutions to improve and converge towards optimal outcomes, and they explore new regions to find possible answers.