Development and Analysis of Metaheuristics for Challenging Large-Scale Optimization Problems

从科学、医疗保健、经济到工程问题，几乎可以在生活的各个方面看到优化的好处。这些领域的重要问题是用精确的方法计算难以解决的。我的研究使用计算智能，这是人工智能的一个分支，用于解决使用传统优化方法过于具有挑战性或无法解决的问题。具体来说，我使用的算法是模拟鸟群的紧急行为（粒子群优化，PSO）和进化算法（ea），灵感来自达尔文进化论和种群遗传学：我使用生物过程，如选择、交叉和突变，来快速找到在实践中可以接受的解决方案，或者“接近完美”的解决方案，最优解决方案可能需要一生的时间来计算。现实世界中的大多数优化问题涉及两个或多个目标的同时优化，通常是相互冲突的目标，例如，在最大限度地提高安全和效率的同时最小化运输成本；这被称为多目标优化问题。基于ea和pso的优化算法以及其他元启发式算法在解决MOPs方面所发挥的作用已得到充分证明。我们越来越多地面临着具有三个以上目标的问题，即MOPs的特殊情况，称为多目标优化问题（MaOPs）。然而，随着目标数量（超过3个）和问题维度或决策变量数量的增加，即对于大规模的多目标优化问题，上述算法的性能会严重恶化，这对优化技术提出了新的挑战。提出的研究将解决有效的算法设计、解决方案评估和数据/算法活动可视化，这些问题在MaOPs中尚未得到充分解决。将研究MaOPs的知名基准函数和网络科学中出现的大规模现实世界优化问题，例如在复杂网络中寻找关键节点。关键节点检测问题有许多实际应用：例如，针对蛋白质相互作用网络中的特定蛋白质用于药物设计，计算机网络安全以保护或攻击重要领域，确定智能电网的脆弱性，以及在减轻疾病传播时识别个体进行疫苗接种或隔离。为重要问题提供有效的算法解决方案，并通过提高我们对不同类别具有挑战性的优化问题的适当算法和技术的理解，本研究将使许多利用不同领域优化的研究人员和实践者受益。我将与研究生和大四本科生一起寻找具有挑战性的优化问题的前沿解决方案，并推进ea和pso的理论方面和发展。

The benefits of optimization can be observed in almost all aspects of life, from science, healthcare, economics, to engineering problems. Important problems in these areas are computationally intractable by exact methods. My research uses computational intelligence, a branch of artificial intelligence to solve problems that are either too challenging or impossible to solve using conventional optimization methods. Specifically, I use algorithms that simulate the emergent behaviour of flocking birds (particle swarm optimization, PSO) and evolutionary algorithms (EAs) inspired by Darwinian evolution and population genetics: I use biological processes such as selection, crossover, and mutation to quickly find solutions that are acceptable in practice, or "near perfect" where the optimal solution may require a lifetime to compute. The majority of real-world optimization problems involve simultaneous optimization of two or more, often conflicting objectives, e.g., minimizing transportation costs while maximizing safety and efficiency; this is called a multi-objective optimization problem (MOP). The successes in the role played by optimizing algorithms based on EAs and PSOs, among other metaheuristics in solving MOPs are well documented. Increasingly we are faced with problems that have more than three objectives, i.e., a special case of MOPs, termed many-objective optimization problems (MaOPs). However, the performance of the aforementioned algorithms deteriorates severely with increase in the number of objectives (beyond three) and the number of problem dimensions or decision variables, i.e., for large-scale many-objective optimization problems, which present new challenges for optimization techniques. The proposed research will tackle efficient algorithm design, solution evaluation, and data/algorithm activity visualizations that have not been adequately addressed for MaOPs. Both well-known benchmark functions for MaOPs and large-scale real-world optimization problems arising in network science, such as finding critical nodes in complex networks, will be investigated. The critical node detection problem has many practical applications: e.g., targeting specific proteins in protein interaction networks for use in drug design, in computer network security so as to protect or attack important areas, in determining smart grid vulnerability, and in identifying individuals for vaccination or quarantine when mitigating disease spread. Providing efficient algorithmic solutions for important problems, and by improving our understanding of appropriate algorithms and techniques for different classes of challenging optimization problems, this research will benefit many researchers and practitioners that utilize optimization across different domains. I will involve graduate and senior undergraduate students to find cutting-edge solutions to challenging optimization problems and to advance the theoretical aspects and development of EAs and PSOs.