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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A corner point-based algorithm to solve constrained multi-objective optimization problems
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A corner point-based algorithm to solve constrained multi-objective optimization problems

机译：基于角点的算法来解决约束的多目标优化问题

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摘要

The search ability of an algorithm in terms of convergence and diversity can be improved with the help of corner points. A corner point-based algorithm (CPA) based on a differential evolution (DE) algorithm is proposed to solve constrained multi-objective optimization problems. The evolutionary algorithm consists of two stages. The first stage is to find corner points by the proposed method. The second stage is to approach the real Pareto front. A novel diversity and convergence mechanism is implemented in the second stage. The performance of the proposed algorithm is evaluated on nineteen test functions. Compared with the constrained handling techniques and latest optimization algorithms, the numerical results have indicated that the proposed algorithm is effective. At last, the algorithm is used to solve resource schedule in emergency management to further validate its effectiveness.
机译：在角点的帮助下，可以改善在收敛和多样性方面的算法的搜索能力。 提出了一种基于差分演进（DE）算法的基于角点的算法（CPA）来解决约束的多目标优化问题。 进化算法由两个阶段组成。 第一阶段是通过所提出的方法找到角点。 第二阶段是接近真正的帕累托前面。 在第二阶段实施了一种新的多样性和收敛机制。 在十九个测试功能中评估了所提出的算法的性能。 与受约束的处理技术和最新优化算法相比，数值结果表明，所提出的算法是有效的。 最后，该算法用于解决紧急管理中的资源计划，以进一步验证其有效性。

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