Research on a new multiobjective combinatorial optimization algorithm

An real world engineering design problem is usually with multiple conflicting objectives, and it is easily lead to the difficulty to optimize these objectives at the same time. Multiobjective combinatorial optimization is not only an open theory problem, but also with an important practical significance. After modeling the constrained multiobjective combinatorial optimization problem, a new optimization algorithm is presented in detail. The algorithm is different from existing multiobjective evolutionary algorithms in three aspects. The first is the two-layer encoding method. The second is that it hybrids the simulated annealing algorithm with the genetic algorithm to improve the global searching ability while maintaining the parallel computing ability. The third is the decision making mechanism to evaluate candidate solutions with several design objectives. A numerical example study shows that the proposed algorithm is capable of dealing with multiobjective combinatorial optimization problems.