A Manufacturing Oriented Single Point Search Hyper-Heuristic Scheme for Multi-Objective Optimization

Multi-objective optimization problems are frequently encountered in engineering analyses. Optimization techniques in practical applications are devised and evaluated mostly for specific problems, and thus may not be generally applicable when applications vary. In this study we formulate a probability matching based hyper-heuristic scheme, then propose four low-level heuristics which can work coherently with the single point search algorithm MOSA/R (Multi-Objective Simulated Annealing Algorithm based on Re-pick) towards multi-objective optimization problems of various properties, namely DTLZ and UF test instances. Making use of the domination amount, crowding distance and hypervolume calculations, the hyper-heuristic scheme could meet different optimization requirements. The approach developed (MOSA/R-HH) exhibits better and more robust performance compared to AMOSA, NSGA-II and MOEA/D as illustrated in the numerical tests. The outcome of this research may potentially benefit various design and manufacturing practices.