To balance the exploration and exploitation in the searching process for different optimization problems and the same optimization problem in different search phases

as well as to reduce the computational cost

an adaptive mating restriction probability-based self-organizing multiobjective evolutionary algorithm (ASMEA) was proposed. Firstly

the self-organizing map (SOM) algorithm was used to establish the neighborhood relationships among the population individuals in ASMEA. The appropriate reproduction based on the above relationship is helpful to produce high quality solutions during the later period of the searching process. To reduce the computational cost of the cluster

the evolutionary algorithm was combined with SOM. At each generation

ASMEA alternately conducts the SOM training step and evolves the population. Secondly

the mating restriction probability was set to control the mating parents selected from both the neighbor population built by SOM and the whole population

so as to strengthen the exploitation and exploration respectively. Lastly

the mating restriction probability was self-adaptively updated in each generation by the utility of generation offspring based on different paternal sources in previous generations. ASMEA and five representative multiobjective evolutionary algorithms were experimented on a number of test instances. Results suggest that ASMEA performed better than the others on search quality

search efficiency

and visual comparison

which verified the ability of ASMEA to solve complicated multiobjective optimization problems.