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- 论文信息
原始英文题目：Preference-inspired co-evolutionary algorithms using weight vectors
英文关键词：Evolutionary algorithms, Multi-objective optimisation, Many-objective, Co-evolution, Weights
中文题目：基于偏好启发的权重共生进化算法
中文关键词：进化算法，多目标优化，多目标问题，共生进化，权重 - 摘要
原始英文摘要：
Decomposition based algorithms perform well when a suitable set of weights are provided; however determining a good set of weights a priori for real-world problems is usually not straightforward due to a lack of knowledge about the geometry of the problem. This study proposes a novel algorithm called preference-inspired co-evolutionary algorithm using weights (PICEA-w) in which weights are co-evolved with candidate solutions during the search process. The co-evolution enables suitable weights to be constructed adaptively during the optimisation process, thus guiding candidate solutions towards the Pareto optimal front effectively. The benefits of co-evolution are demonstrated by comparing PICEA-w against other leading decomposition based algorithms that use random, evenly distributed and adaptive weights on a set of problems encompassing the range of problem geometries likely to be seen in practice, including simultaneous optimisation of up to seven conflicting objectives. Experimental results show that PICEAw outperforms the comparison algorithms for most of the problems and is less sensitive to the problem geometry.
中文摘要：
当提供了一套合适的权重时，分解算法表现良好；然而，由于缺乏对问题几何形状的了解，预先确定现实世界问题的一组好权重通常并不直接。本研究提出了一种名为基于偏好启发的权重共生进化算法（PICEA-w）的新算法，该算法在搜索过程中与候选解一起共生进化权重。共生进化使得权重能够在优化过程中自适应地构建，从而有效地引导候选解朝着Pareto最优前沿发展。通过将PICEA-w与其他使用随机、均匀分布和自适应权重的领先分解算法在包括可能在实践中遇到的各种问题几何形状的一组问题上进行比较，证明了共生进化的好处，包括同时优化多达七个冲突目标。实验结果表明，PICEAw在大多数问题上的表现超过了比较算法，并且对问题几何形状的敏感性较低。
- 创新点与学术价值
原始创新点：
The main innovation of this study is the proposal of a novel algorithm, PICEA-w, that adaptively modifies weights by co-evolving them with candidate solutions during the search process, which is a significant departure from traditional decomposition-based algorithms that rely on pre-specified weights.
中文创新点：
本研究的主要创新是提出了一种新算法PICEA-w，它在搜索过程中通过与候选解一起共生进化权重来自适应地修改权重，这与依赖预先指定权重的传统分解算法有显著的不同。
学术价值：
PICEA-w算法的提出为多目标优化问题提供了一种新的解决方案，特别是在处理具有复杂Pareto前沿的问题时，能够更有效地指导优化过程，提高解的质量。这种算法通过自适应地调整权重，减少了对问题先验知识的依赖，增强了算法的通用性和鲁棒性，对于实际应用中的问题解决具有重要的理论和实践意义。
- 结论
原始英文结论：
This study has proposed a new decomposition based algorithm for multi-objective optimisation, PICEA-w, that eliminates the need to specify appropriate weights in advance of performing the optimisation. Through rigorous empirical testing, we have demonstrated the benefits of PICEA-w compared to other leading decomposition based algorithms. The main findings are that PICEA-w is less sensitive to the problem geometry, and outperforms other leading decomposition based algorithms on many-objective problems.
中文结论：
本研究提出了一种新的多目标优化分解算法PICEA-w，它消除了在执行优化之前需要指定适当权重的需要。通过严格的实证测试，我们展示了PICEA-w与其他领先的分解算法相比的优势。主要发现是PICEA-w对问题几何形状的敏感性较低，在多目标问题上的表现超过了其他领先的分解算法。
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