基于自适应截断策略的约束多目标优化算法

为提高约束多目标优化问题所求解集的分布性和收敛性，该文提出基于自适应截断策略的约束多目标优化算法。首先，自适应截断选择策略能够保留Pareto最优解和约束违反度及目标函数值均较优的不可行解，不仅提高了种群多样性，而且能够较好地兼顾多样性和收敛性；其次，为增强算法的局部开发能力，在变异操作和交叉操作之后进行指数变异；最后，改进的拥挤密度估计方式只选择一部分Pareto最优解和距离较近的个体参与计算，不仅更加准确地反映解集的分布性，而且降低了计算量。通过在标准测试问题(CTP系列)上与其他4种优秀算法的对比结果可以得出，该算法所求解集的分布性和收敛性均得到一定提高，而且相较于对比算法在求解性能上具备一定的优势。

Constrained Multi-objective Optimization Algorithm with Adaptive Truncation Strategy

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents a constrained multi-objective optimization algorithm based on adaptive truncation strategy. Firstly, through the proposed truncation strategy, the Pareto optimal solutions and the infeasible solutions with low constraint violation and good objective function values are retained to improve diversity. Besides, both diversity and convergence are coordinated. Secondly, the exponential variation is added for further enhancing the local exploitation ability after mutation and crossover operation. Finally, the improved crowding density estimation chooses a part of the Pareto optimal individuals and the near individuals to take part in the calculation, thus it not only assesses the distribution of the solution set more accurately, but also reduces the computational quantity. The comparative experiment results with another four excellent constrained multi- objective algorithms on the standard constrained multi-objective optimization problems (CTP series) show that diversity and convergence of the proposed algorithm are improved, and it has certain advantages compared with these algorithms.