14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022, Virtual, Online, 14 - 16 Aralık 2022, cilt.648 LNNS, ss.690-699
Even though multi-objective optimization problems’ solution space is inherently complex, heuristic based algorithms often search the solution space by a single neighbour creation technique. Dynamic neighbour generation (DNG) allows searching solution space by multiple heuristic operators and brings a new perspective to neighbour creation process especially for the multi-objective optimization problems. This paper presents extensive comparative experiments for the purpose of analyzing and revealing the achievement of our proposed DNG framework on a set of benchmark problems. DNG is integrated with the fast and elitist multi-objective genetic algorithm (NSGA-II), multi-objective migrating birds optimization algorithm (MMBO), the strength Pareto evolutionary algorithm 2 (SPEA2) and Pareto simulated annealing (PSA). Multi-objective hyper-heuristic evolutionary algorithm (MHypEA) is also implemented for obtaining a more effective comparison. Experiments demonstrate that DNG based versions of algorithms are 24.39% and 28.35% better on the average than their original variants in terms of inverted generational distance indicator and in terms of hypervolume indicator respectively.