Abstract Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone
trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic …

One of the major problems in Big Data is a large number of features or dimensions, which
causes the issue of “the curse of dimensionality” when applying machine learning …

Ant Colony Optimization (ACO) is a metaheuristic proposed by Marco Dorigo in 1991 based
on behavior of biological ants. Pheromone laying and selection of shortest route with the …

A swarm is a large number of homogenous, simple agents interacting locally among
themselves, and their environment, with no central control to allow a global interesting …

Animals are often faced with time-critical decisions without prior information about their
actions' outcomes. In such scenarios, individuals budget their investment into the task to cut …

Combined heat and power economic dispatch (CHPED) is an energy management problem
that minimizes the operation cost of power and heat generation while a vast variety of …

针对已有的适用于分配任务的蚁群算法易陷入局部最优解的缺陷, 提出了一个保证云服务质量的
分组多态蚁群算法. 该算法将蚁群按职能不同分为搜索蚁, 侦察蚁和工蚁, 根据预测完成时间的 …

Computational biology has changed how healthcare systems and biomedical engineering
work. Nature-inspired intelligent computing (NIIC) approaches in predicting potential …

Background: Bioinspired computing algorithms are population-based probabilistic search
optimization approaches inspired by biological evolution and activity. These are highly …

Program trace alignment is the process of establishing a correspondence between dynamic
instruction instances in executions of two semantically similar but syntactically different …