Abstract:
An achievement scalarizing function search-based multi-objective evolutionary algorithm is presented to overcome the decrease of population diversity and premature convergence of multi-objective evolutionary algorithm based on decomposition caused by the fixed population size and set of weight vectors during the evolutionary process. In the proposed algorithm, the smaller initial population is first set, and a local search strategy based on achievement scalarizing function with adaptive preference is designed to enhance the search of the sparse region and dynamically increase the population size and weight vector. Then a hybrid differential evolution operator with adaptive scaling factor is proposed to balance global exploration and local exploitation. Different from the existing algorithms, variable population size and dynamically increasing weight vector can avoid the decrease of population diversity and premature convergence. The proposed algorithm is compared with 11 typical algorithms on 10 benchmark functions. Experimental results show that the proposed algorithm obtains a uniform distribution set close to the Pareto front.
