Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm.

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

Distributed Memetic Algorithm for Outsourced Database Fragmentation.

Data privacy and utility are two essential requirements in outsourced data storage. Traditional techniques for sensitive data protection, such as data encryption, affect the efficiency of data query and evaluation. By splitting attributes of sensitive associations, database fragmentation techniques can help protect data privacy and improve data utility. In this article, a distributed memetic algorithm (DMA) is proposed for enhancing database privacy and utility. A balanced best random distributed framework is designed to achieve high optimization efficiency. In order to enhance global search, a dynamic grouping recombination operator is proposed to aggregate and utilize evolutionary elements; two mutation operators, namely, merge and split, are designed to help arrange and create evolutionary elements; a two-dimension selection approach is designed based on the priority of privacy and utility. Furthermore, a splicing-driven local search strategy is embedded to introduce rare utility elements without violating constraints. Extensive experiments are carried out to verify the performance of the proposed DMA. Furthermore, the effectiveness of the proposed distributed framework and novel operators is verified.

Distributed Differential Evolution Based on Adaptive Mergence and Split for Large-Scale Optimization.

Nowadays, large-scale optimization problems are ubiquitous in many research fields. To deal with such problems efficiently, this paper proposes a distributed differential evolution with adaptive mergence and split (DDE-AMS) on subpopulations. The novel mergence and split operators are designed to make full use of limited population resource, which is important for large-scale optimization. They are adaptively performed based on the performance of the subpopulations. During the evolution, once a subpopulation finds a promising region, the current worst performing subpopulation will merge into it. If the merged subpopulation could not continuously provide competitive solutions, it will be split in half. In this way, the number of subpopulations is adaptively adjusted and better performing subpopulations obtain more individuals. Thus, population resource can be adaptively arranged for subpopulations during the evolution. Moreover, the proposed algorithm is implemented with a parallel master-slave manner. Extensive experiments are conducted on 20 widely used large-scale benchmark functions. Experimental results demonstrate that the proposed DDE-AMS could achieve competitive or even better performance compared with several state-of-the-art algorithms. The effects of DDE-AMS components, adaptive behavior, scalability, and parameter sensitivity are also studied. Finally, we investigate the speedup ratios of DDE-AMS with different computation resources.