Self Adaptive Learning – Great Deluge Based Hyper-heuristics for Solving Cross Optimization Problem Domains

Abstract

In the literature, almost all optimization problems in NP-hard class are solved by meta-heuristics approach. However, this approach has the drawback of requiring tuning parameters for each different problem domain and different instances of the same problem. This approach is considered less effective in resolving these problems. Therefore, a new approach is needed, namely the hyper-heuristics approach that is able to solve cross-domain problems. Hyper-heuristic is one of the approximate search methods which is able to provide solutions to NP-hard problems in polynomial time, as well as giving fairly good and acceptable results. This method has two properties of search space, namely the selection of LLH and the acceptance of solutions (move acceptance). This approach works in barrier domains rather than directly working in problem domains. With these properties, hyper-heuristic is able to solve problems in different domains. In addition, hyper-heuristics has a learning mechanism through feedback from previously generated solutions. This final project tries to apply a hyperheuristic algorithm in six combinatorial optimization problem domains, namely SAT, Bin Packing, Flow Shop, Personnel Scheduling, TSP, and VRP. The method that will be used in this final project is Self Adaptive - Great Deluge (SAD-GED). The Self Adaptive mechanism is used to make LLH selection to be used, while the Great Deluge is used in determining the acceptance of solutions (move acceptance) in a hyperheuristic framework. The application of the SAD-GED algorithm is expected to be able to provide better results than the existing algorithm used previously, namely Simple Random - Simulated Annealing.