  In the field of evolutionary multiobjective optimization, the decision maker
(DM) concerns conflicting objectives. In the real-world applications, there
usually exist more than one DM and each DM concerns parts of these objectives.
Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict
the MOP with multiple decision makers involved, where each party concerns about
certain some objectives of all. However, in the evolutionary computation field,
there is not much attention paid on MPMOPs. This paper constructs a series of
MPMOPs based on distance minimization problems (DMPs), whose Pareto optimal
solutions can be vividly visualized. To address MPMOPs, the new proposed
algorithm OptMPNDS3 uses the multiparty initializing method to initialize the
population and takes JADE2 operator to generate the offsprings. OptMPNDS3 is
compared with OptAll, OptMPNDS and OptMPNDS2 on the problem suite. The result
shows that OptMPNDS3 is strongly comparable to other algorithms
