人类群体优化器（HGO）：模仿人类群体的集体智慧，作为组合问题的优化工具
The Human Group Optimizer (HGO): Mimicking the collective intelligence of human groups as an optimization tool for combinatorial problems
摘要：
研究人员开发了大量的优化算法来解决运营管理领域的各种复杂问题。我们提出了一种新的优化算法属于类的群智能优化方法。该算法模仿人类群体的决策过程，并利用这一过程的动态作为组合问题的优化工具。为了实现这一目标，提出了一个连续时间马尔可夫过程来描述一个群体的社会相互作用的代理人的行为，模拟人类在一个群体中如何修改他们的意见驱动的自我利益和共识寻求。如在自旋集合的情况下，这种系统的动力学的特征在于总共相（磁化）从低值到高值的相变。我们认识到，这种相变与人口中集体优越智力的出现有关。当这种状态处于活动状态时，应用冷却时间表使代理越来越接近最优解，同时在适应度景观上执行随机行走。比较与模拟退火，以及与多代理版本的模拟退火的效率方面找到良好的解决方案的NK -考夫曼景观。在所有情况下，我们的方法优于其他人，特别是在代理有限的知识存在。
英文摘要：
A large number of optimization algorithms have been developed by researchers to solve a variety of complex problems in operations management area. We present a novel optimization algorithm belonging to the class of swarm intelligence optimization methods. The algorithm mimics the decision making process of human groups and exploits the dynamics of this process as an optimization tool for combinatorial problems. In order to achieve this aim, a continuous-time Markov process is proposed to describe the behavior of a population of socially interacting agents, modelling how humans in a group modify their opinions driven by self-interest and consensus seeking. As in the case of a collection of spins, the dynamics of such a system is characterized by a phase transition from low to high values of the overall consenus (magnetization). We recognize this phase transition as being associated with the emergence of a collective superior intelligence of the population. While this state being active, a cooling schedule is applied to make agents closer and closer to the optimal solution, while performing their random walk on the fitness landscape. A comparison with simulated annealing as well as with a multi-agent version of the simulated annealing is presented in terms of efficacy in finding good solution on a NK - Kauffman landscape. In all cases our method outperforms the others, particularly in presence of limited knowledge of the agent.
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