Multi-objective embarrassingly parallel search with upper bound constraints

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. In our previous study, we proposed the multi-objective embarrassingly parallel search (MO-EPS) for multi-objective constraint optimization, which combines two strategies: a constraint programming-based strategy to determine Pareto front and a parallel search for constraint programming. In this study, we propose the MO-EPS with upper bound constraints, an extended algorithm of the MO-EPS.

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