Oppositional concepts in population-based problem solving

基于人口的问题解决中的对立概念

• 批准号: 250386-2008
• 负责人: Tizhoosh, HamidReza
• 金额: $ 1.42万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503485
• 关键词: Oppositional; concepts; population; based; problem

Population-based optimization techniques such as genetic algorithms, differential evolution and ant colonies have diverse applications. These optimization methods have proven to be useful in many cases where conventional optimization methods encounter their applicability limits. However, population-based schemes have their own limitations. Specifically, they may often need considerable computational time to find a solution. This disadvantage becomes more visible the larger the population is, which is (almost) always the case when we deal with high-dimensional and/or complex optimization problems. Hence, methods by which to increase the speed of these techniques have been under investigation for quite some time. The main focus of this research will centre on development of methods to increase the speed of differential evolution and ant colonies. Oppositional concepts will be employed to accelerate the convergence of the methods while maintaining the necessary level of solution accuracy. Opposition-based approaches to optimization generally incorporate the simultaneous consideration of the solution and the opposite solution (chromosome and anti-chromosome, path and opposite path). Recent achievements in the successful design and use of opposition-based differential evolution encourage us to seek some fundamental answers with respect to a mathematical formalism for these techniques and to exploit the potentials of oppositional schemes for all population-based algorithms. Standard benchmark functions and metrics will be used to verify the better performance of opposition-based extensions of methods under investigation. As a real-world test case, segmentation of medical images, specifically breast and prostate ultrasound images, will be undertaken as well. Population-based methods have been used to extract objects from digital images in different ways. Their results, as reported in literature, are in some cases impressive. However, processing images with these methods are extremely expensive. This has restricted their use in practical cases. Any level of speedup is desirable here. Image data sets along with radiologist's ground-truth are available for experimental performance verification.

基于种群的优化技术，如遗传算法，差分进化和蚁群有着广泛的应用。这些优化方法已被证明是有用的，在许多情况下，传统的优化方法遇到其适用性限制。然而，基于人口的计划有其自身的局限性。具体地说，它们可能经常需要相当长的计算时间来找到解决方案。人口越多，这种缺点就越明显，当我们处理高维和/或复杂的优化问题时，情况（几乎）总是如此。因此，提高这些技术的速度的方法已经研究了相当长的一段时间。这项研究的主要重点将集中在开发方法，以提高差异进化和蚁群的速度。对立的概念将被用来加速收敛的方法，同时保持必要的解决方案的精度水平。基于对立的优化方法通常包括同时考虑解决方案和对立解决方案（染色体和反染色体，路径和对立路径）。最近的成就，成功的设计和使用的反对为基础的差分进化鼓励我们寻求一些基本的答案，这些技术的数学形式主义，并利用所有人口为基础的算法的反对计划的潜力。标准的基准函数和指标将被用来验证更好的性能的反对为基础的扩展的方法正在调查。作为一个真实世界的测试案例，医学图像，特别是乳腺和前列腺超声图像的分割，也将进行。基于群体的方法已经被用于以不同的方式从数字图像中提取对象。他们的结果，在文献中报道，在某些情况下是令人印象深刻的。然而，用这些方法处理图像是非常昂贵的。这限制了它们在实际情况中的使用。任何级别的加速都是可取的。图像数据集沿着放射科医师的真实数据可用于实验性能验证。