The intestinal tract is the largest digestive organ in the human body. It is colonized by, and consistently exposed to, a myriad of microorganisms, including bifidobacteria, lactobacillus, Escherichia coli, enterococcus, clostridium perfringens, and pseudomonas. To protect the body from potential pathogens, the intestinal tract has evolved regional immune characteristics. These characteristics are defined by its unique structure, function, and microenvironment, which differ drastically from those of the common central and peripheral immune organs. The intestinal microenvironment created by the intestinal flora and its products significantly affects the immune function of the region. In turn, specific diseases regulate and influence the composition of the intestinal flora. A constant interplay occurs between the intestinal flora and immune system. Further, the intestinal microenvironment can be reconstructed by probiotic use or microbiota transplantation, functioning to recalibrate the immune homeostasis, while also contributing to the treatment or amelioration of diseases. In this review, we summarize the relationship between the intestinal flora and the occurrence and development of diseases as an in-turn effect on intestinal immunity. We also discuss improved immune function as it relates to non-specific and specific immunity. Further, we discuss the proliferation, differentiation and secretion of immune cells, within the intestinal region following remodeling of the microenvironment as a means to ameliorate and treat diseases. Finally, we suggest strategies for improved utilization of intestinal flora.

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In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations. However, most state-of-the-art MOEAs show poor performance in balancing them, and can easily cause the working populations to concentrate on a part of regions of the Pareto fronts, leading to a serious imbalanced searching between preserving diversity and achieving convergence. This paper proposes a method which combines a multi-objective to multi-objective (M2M) approach with the push and pull search (PPS) framework, namely PPS-M2M. To be more specific, the proposed algorithm decomposes a CMOP into a set of simple CMOPs. Each simple CMOP corresponds to a sub-population and is solved in a collaborative manner. When dealing with constraints, each sub-population follows a procedure of "ignore the constraints in the push stage and consider the constraints in the pull stage", which helps each working sub-population get across infeasible regions. In order to evaluate the performance of the proposed PPS-M2M, it is compared with the other six algorithms, including M2M, MOEA/D-Epsilon, MOEA/D-SR, MOEA/D-CDP, C-MOEA/D and NSGA-II-CDP on a set of benchmark CMOPs. The experimental results show that the PPS-M2M is significantly better than the other six algorithms.

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