Akademik Veri Yönetim Sistemi
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.