A Comparative Study On Three Hyper-Heuristic Approaches For Solving Benchmark Scheduling Problems

Abstract

The research work compares the outcome and solving capabilities of three prominent algorithms. Each algorithm are separately implemented as higher level heuristic to manage the group of low level heuristics (LLHs) in order to solve the benchmark university scheduling instances. The study comprises over Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Evolutionary Algorithm (EA). All these optimization techniques are highly appraised for their skills to handle the complex problems. A number of classical operators and parameters have been examined with each hyper-heuristics due to high diversity in datasets. Secondly, Domain specified Low Level Heuristics have been designed under several operational classifications. In addition, obtaining effective deployment and utilization of the academic resources to the greatest extent are counted as supplementary but essential advantages of the research work.