Oppositional concepts in population-based problem solving

基于群体的优化技术，如遗传算法、差分进化和蚁群有不同的应用。这些优化方法已被证明在许多常规优化方法遇到其适用性限制的情况下是有用的。然而，以人口为基础的计划有其自身的局限性。具体来说，它们可能经常需要大量的计算时间来找到解决方案。人口越多，这个缺点就越明显，当我们处理高维和/或复杂的优化问题时（几乎）总是这样。因此，提高这些技术的速度的方法已经被研究了相当长的一段时间。这项研究的主要重点将集中在提高差异进化和蚁群速度的方法的发展上。将采用对立概念来加速方法的收敛，同时保持必要的解精度水平。基于对立的优化方法通常包括同时考虑解和相反解（染色体和反染色体，路径和相反路径）。最近在基于对立的差分进化的成功设计和使用方面取得的成就鼓励我们寻找一些关于这些技术的数学形式的基本答案，并为所有基于种群的算法开发对立方案的潜力。将使用标准基准函数和度量来验证基于对立的方法扩展的更好性能。作为一个现实世界的测试案例，医学图像的分割，特别是乳房和前列腺超声图像，也将进行。基于种群的方法已被用于以不同的方式从数字图像中提取物体。据文献报道，他们的结果在某些情况下令人印象深刻。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案例中的使用。在这里，任何级别的加速都是可取的。图像数据集以及放射科医生的基础事实可用于实验性能验证。

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.