Discretization of nature-inspired techniques for combinatorial problems

Abstract

Scientists across various domains like scheduling, computational biology, and machine learning face constraint-solving and optimization problems. Classical systematic and mathematical methods often fall short of providing suitable solutions for such complex problems, leading to the introduction of metaheuristic algorithms. These algorithms exhibit diverse characteristics and can effectively address specific optimization problems. The primary motivation is to develop robust metaheuristics that can efficiently handle scaling problems. However, one challenge with metaheuristics is their immature convergence. In the context of Constraint Satisfaction Problems (CSPs), a framework applicable to numerous real-world problems, metaheuristics play a significant role. To address these objectives and challenges, this thesis investigates the applicability of metaheuristics, including the Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Water Cycle Algorithm (WCA). More precisely, we propose a discrete version of nature-inspired techniques for solving the Electricity Technician Dispatch Problems (ETDP), the Nurse Scheduling Problem (NSP), and the Task Scheduling Problem in Mobile Cloud Computing. We also propose a discrete version of WOA for Type 2 Diabetes Diagnosis. Experimentation showcases the efficiency of the proposed techniques in finding a good trade-off between running time and the quality of the solution returned.