Multi-objective embarrassingly parallel search for constraint programming

Optimization plays an important role in various disciplines of engineering. Multi-objective optimization is usually characterized by a Pareto front. In large scale multi-objective optimization problems, determining an optimal Pareto front consumes large time. Thus, parallel computing is used to speed up the search. Constraint programming is one of the logic-based optimization techniques for solving combinatorial optimization problems. Kotecha et al. proposed a constraint programming-based strategy to determine an optimal Pareto front. Regin et al. proposed a parallel search for constraint programming, called the embarrassingly parallel search. In this paper, we propose the multi-objective embarrassingly parallel search for multi-objective constraint optimization, which combines the two strategies.