The Hybrid Framework for Multi-objective Evolutionary Optimization Based on Harmony Search Algorithm

Abstract

In evolutionary multi-objective optimization, an evolutionary algorithm is invoked to solve an optimization problem involving concurrent optimization of multiple objective functions. Many techniques have been proposed in the literature to solve multi-objective optimization problems including NSGA-II, MOEA/D and MOPSO algorithms. Harmony Search (HS), which is a relatively new heuristic algorithm, has been successfully used in solving multi-objective problems when combined with non-dominated sorting (NSHS) or the breakdown of the multi-objectives into scalar sub-problems (MOHS/D). In this paper, the performance of NSHS and MOHS/D is enhanced by using a previously proposed hybrid framework. In this framework, the diversity of the population is measured every a predetermined number of iterations. Based on the measured diversity, either local search or a diversity enhancement mechanism is invoked. The efficiency of the hybrid framework when adopting HS is investigated using the ZDT, DTLZ and CEC2009 benchmarks. Experimental results confirm the improved performance of the hybrid framework when incorporating HS as the main algorithm.