A multipopulation evolutionary framework with Steffensen’s method for dynamic multiobjective optimization problems

Dynamic multiobjective optimization problems (DMOPs) require the evolutionary algorithms that can track the moving Pareto-optimal fronts efficiently. This paper presents a dynamic multiobjective evolutionary framework (DMOEF-MS), which adopts a novel multipopulation structure and Steffensen’s method to solve DMOPs. In DMOEF-MS, only one population deals with the original DMOP, while the others focus on single-objective problems that are generated by the weighted summation of the original DMOP. Then, Steffensen’s method is used to control the evolving process in two ways: prediction and diversity-maintenance. Particularly, the prediction strategy is devised to predict the next promising positions for the individuals that handle single-objective problems, and the diversity-maintenance strategy is used to increase population diversity before the environment changes and reinitialize the multiple populations after the environment changes. This paper gives a comprehensive comparison of DMOEF-MS with some state-of-the-art DMOEAs on 14 DMOPs and the experimental results demonstrate the effectiveness of the proposed algorithm.

Structure and Dynamics of the Breast Tissue Microbiomes Under Tumor Influences: An Approach With Neutral, Near-Neutral, and Niche-Neutral Hybrid Models

The structure and dynamics of breast tissue bacteria can have far-reaching influences on women’s health, particularly on breast tumor development. However, there is little understanding on the ecological processes that shape the structure and dynamics of breast tissue bacteria. Here, we fill the gap by applying three metacommunity models for investigating the community assembly and diversity maintenance, including Sloan near neutral model, Harris et al. multisite neutral and Tang & Zhou niche-neutral hybrid models to reanalyze the 16S-rRNA sequencing datasets of 23 healthy, 12 benign tumor, and 33 malignant tumor tissue samples. First, we found that, at the community/metacommunity levels, the mechanisms of bacteria assembly and diversity maintenance of breast tissue bacteria were moderately influenced by stochastic drifts of bacteria demography (division, death, and dispersal of bacterial cells). At species level, on average, approximately 10 and 5% species were above (positively selected) and below (negatively selected) neutral, respectively. Furthermore, malignant tumor may raise the positively selected species up to 17%. Second, malignant tumor appears to inhibit microbial dispersal as evidenced by lowered migration rates, compared with the migration in normal and benign tumor tissues. These theoretic findings can be inspirational for further investigating the relationships between tissue bacteria and breast tumor progression/development.

Cross-Scale Analyses of Animal and Human Gut Microbiome Assemblies from Metacommunity to Global Landscape

Understanding how the coevolution (evolutionary time scale) and/or the interactions (ecological time scale) between animal (human) gut microbiomes and their hosts shape the processes of the microbiome assembly and diversity maintenance is important but rather challenging. An effort may start with the understanding of how and why animals and humans may differ in their microbiome neutrality (stochasticity) levels.

An intelligent algorithm based on evolutionary strategy and clustering algorithm for Lamb wave defect location

Imaging algorithms for visualization of defects play a significant role in Lamb wave–based research of nondestructive testing and structural health monitoring. In classical algorithms, the position or distribution of defects is located by mapping the amplitude or phase information of signals from the time domain to every discrete spatial grid of the structure. It is time-consuming. In this study, the diversity, statistical, and fuzzy characteristics of the elliptic imaging algorithm are analyzed first; then, an intelligent defect location algorithm is proposed based on the evolutionary strategy and the K-means algorithm. The position of defects can be identified by observing the distribution of individuals. There are six parts in the proposed algorithm, including the data structure design, adaptive population screening, adaptive population reproduction, diversity maintenance mechanism, and cutoff criterion. Considering the statistical and fuzzy characteristics in the detection, several specific input parameters are defined in our algorithm, such as the distance-dependent screening threshold, path-dependent residual vector, and path-independent residual. To maintain the diversity of individuals in the analysis, we have made two adjustments to the evolutionary strategy: one is to optimize the population screening and reproduction steps with the K-means algorithm, and the other is to add a diversity maintenance method into the evolutionary strategy. The effectiveness of the proposed intelligent defect location algorithm is verified by numerical simulations and experiments. Numerical studies indicate that the proposed algorithm has a reliable performance in the detection of defects with different shapes and sizes. In the experimental research, we demonstrate that the efficiency of the proposed algorithm is about 200 times faster than the elliptic imaging algorithm. And the optimum parameter setting of the algorithm is investigated by analyzing the influence of parameter setting on the detection.