A Choice Function based hyper-heuristic for Multi-objective Optimization

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics to solve difficult computational optimization problems. There are two main types of hyper-heuristics: selective and generative hyper-heuristics. An online selective hyper-heuristic framework manages a set of low level heuristics and aims to choose the best one at any given time using a performance measure for each low level heuristic. In this study, we propose a selective hyper-heuristic choice function based to solve multi-objective optimization problems. This hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA) as the low level heuristics. The choice function heuristic selection method uses a scheme which ranks four performance measurements. All-Moves is employed as an acceptance strategy, meaning that we accept the output of each low level heuristic whether it improves the quality of the solution or not. Our proposed approached compared to the three low level heuristics when used in isolations and two multi-objective hyper-heuristics; a random hyper-heuristics and an adaptive multi-method search namely (AMALGAM). The experimental results demonstrate the effectiveness of the hyper-heuristic approach over the WFG test suite, a common benchmark for multi-objective optimization.