New version of a multi-algorithm genetically adaptive for multiobjective optimization
The proposed MMTD employs both Pareto dominance-based concept and decomposition strategy in the process of population evolution. The proposed algorithm employs these multimethod simultaneously based on dynamic resources allocation procedure. The proposed MMTD is flexible and many algorithms or multiple operators in their constituent algorithms can be integrated in their evolutionary process. The impact analysis of different operators in constituent algorithms of the MMTD framework is part of work in this paper. Experimental results demonstrate that MMTD has significantly performed better than state-of-the-art MOEAs such as MOEA/D, NSGA-II, AMALGAM, MOPSO: a proposal for multiple objective particle swarm optimization, MOSaDE, DECMOSA-SQP in terms of reducing the inverted generational distance (IGD) metric and time cost on both ZDT test problems [68] and CEC09 test instances.
multi-objective optimization evolutionary algorithms pareto front multiobjective memetic algorithm (momas)