This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing with problems with irregular Pareto front. The proposed algorithm does not need to deal with the issues of predefining weight vectors and calculating indicators in the search process. It is mainly based on the thought of adaptively selecting multiple promising search directions according to crowdedness information in local objective spaces. Concretely, the proposed algorithm attempts to dynamically delete an individual of poor quality until enough individuals survive into the next generation. In this environmental selection process, the proposed algorithm considers two or three individuals in the most crowded area, which is determined by the local information in objective space, according to a probability selection mechanism, and deletes the worst of them from the current population. Thus, these surviving individuals are representative of promising search directions. The performance of the proposed algorithm is verified and compared with seven state-of-the-art algorithms [including four general multi/many-objective EAs and three algorithms specially designed for dealing with problems with irregular Pareto-optimal front (PF)] on a variety of complicated problems with different numbers of objectives ranging from 2 to 15. Empirical results demonstrate that the proposed algorithm has a strong competitiveness power in terms of both the performance and the algorithm compactness, and it can well deal with different types of problems with irregular PF and problems with different numbers of objectives.

中文翻译：

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基于目标空间定位选择的多目标进化算法

本文提出了一种简单而有效的多目标进化算法（EA）来处理不规则的帕累托前沿问题。所提出的算法不需要处理搜索过程中预先定义权重向量和计算指标的问题。它主要基于根据局部目标空间中的拥挤度信息自适应选择多个有希望的搜索方向的思想。具体来说，所提出的算法试图动态删除一个质量差的个体，直到有足够的个体存活到下一代。在这个环境选择过程中，所提出的算法根据目标空间中的局部信息，根据概率选择机制，考虑最拥挤区域中的两个或三个个体，并从当前人口中删除其中最差的。因此，这些幸存的个体代表了有希望的搜索方向。对所提出算法的性能进行了验证，并与七种最先进的算法[包括四种通用多/多目标 EA 和三种专门为处理不规则帕累托最优前沿 (PF) 问题而设计的算法] 进行了比较目标数从2到15的各种复杂问题。 实证结果表明，该算法在性能和算法紧凑性方面具有很强的竞争力，可以很好地处理不同类型的问题不规则 PF 和不同目​​标数量的问题。这些幸存者代表了有希望的搜索方向。对所提出算法的性能进行了验证，并与七种最先进的算法[包括四种通用多/多目标 EA 和三种专门为处理不规则帕累托最优前沿 (PF) 问题而设计的算法] 进行了比较目标数从2到15的各种复杂问题。 实证结果表明，该算法在性能和算法紧凑性方面具有很强的竞争力，可以很好地处理不同类型的问题不规则 PF 和不同目​​标数量的问题。这些幸存者代表了有希望的搜索方向。对所提出算法的性能进行了验证，并与七种最先进的算法[包括四种通用多/多目标 EA 和三种专门为处理不规则帕累托最优前沿 (PF) 问题而设计的算法] 进行了比较目标数从2到15的各种复杂问题。 实证结果表明，该算法在性能和算法紧凑性方面具有很强的竞争力，可以很好地处理不同类型的问题不规则 PF 和不同目​​标数量的问题。