AI Strategies for Complex Math Problems: Optimization

Finding efficient solutions to hard combinatorial optimization problems is one of the prominent challenges of modern computing. The quest to improve those has been innovated by the advent of artificial intelligence (AI) that has reshaped the quest and enhanced the same with new heuristics improving those algorithms by the format. The first section of the paper explains why algorithm optimization is required in modern computing With the ongoing explosion of data generation and the need for larger and more complex compute tasks, there is an ever-growing demand for algorithms that can produce solutions to mathematical problems in seconds and with high fidelity. In addition, metaheuristic algorithms form general-purpose optimization frameworks capable of enhancing various algorithms, without relying on the problem domain. Here, metaheuristic algorithms (simulated annealing, ant farm optimization, particle swarm optimization, etc.) are robust and flexible algorithms capable of exploring large search spaces to find optimal or near-optimal solutions to different remedial tasks.