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A hybridmany-objective optimization algorithm for coal green production problem

机译:一种用于煤绿色生产问题的杂交实物客观优化算法

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The problem of convergence and diversity in the course of population evolution is difficult to be balanced for solving the many-objective optimization problem (MaOP). To track with the problem, a many-objective optimization algorithm is designed. In the algorithm, a hybrid selection mechanism under the concurrent integration strategy is built to improve algorithm performance by employing the different selection operators. The concurrent integration strategy can select the suitable operator to balance the convergence and diversity of the solution in the course of the population evolutionary. To verify the effectiveness of the algorithm, the designed algorithm is compared with other five excellent many-objective algorithms on DTLZ and WFG test problem. What is more, the designed algorithm is applied to solve the coal green production optimization problem. The simulation results show that the performance of designed algorithm is superior to whether the DTLZ and WFG test problem or the application problem.
机译:为了解决多目标优化问题(MAOP),难以平衡人口演化过程中收敛和多样性的问题。要追踪问题,设计了一种多目标优化算法。在算法中,建立了一个混合选择机制,以通过采用不同的选择运算符来提高算法性能。并发整合策略可以选择合适的运营商,以平衡群体进化过程中解决方案的收敛和多样性。为了验证算法的有效性,将设计的算法与DTLZ和WFG测试问题的其他五种优秀的许多客观算法进行了比较。更重要的是,设计的算法应用于解决煤绿色生产优化问题。仿真结果表明,设计算法的性能优于DTLZ和WFG测试问题还是应用问题。

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