The Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization

A hybrid framework combining the branch and bound method with multiobjective evolutionary algorithms is proposed for nonconvex multiobjective optimization. The hybridization exploits the complementary character of the two optimization strategies. A multiobjective evolutionary algorithm is intended for inducing tight lower and upper bounds during the branch and bound procedure. Tight bounds such as the ones derived in this way can reduce the number of subproblems that have to be solved. The branch and bound method guarantees the global convergence of the framework and improves the search capability of the multiobjective evolutionary algorithm. An implementation of the hybrid framework considering NSGA-II and MOEA/D-DE as multiobjective evolutionary algorithms is presented. Numerical experiments verify the hybrid algorithms benefit from synergy of the branch and bound method and multiobjective evolutionary algorithms

Mesenchymal stem cells and induced pluripotent stem cells as therapies for multiple sclerosis.

Multiple sclerosis (MS) is a chronic, autoimmune, inflammatory demyelinating disorder of the central nervous system that leads to permanent neurological deficits. Current MS treatment regimens are insufficient to treat the irreversible neurological disabilities. Tremendous progress in the experimental and clinical applications of cell-based therapies has recognized stem cells as potential candidates for regenerative therapy for many neurodegenerative disorders including MS. Mesenchymal stem cells (MSC) and induced pluripotent stem cell (iPSCs) derived precursor cells can modulate the autoimmune response in the central nervous system (CNS) and promote endogenous remyelination and repair process in animal models. This review highlights studies involving the immunomodulatory and regenerative effects of mesenchymal stem cells and iPSCs derived cells in animal models, and their translation into immunomodulatory and neuroregenerative treatment strategies for MS

Explicitly B-preinvex functions

AbstractThis paper introduces a new class of functions, to be referred to as explicitly B-preinvex functions. Some properties of explicitly B-preinvex functions are established, e.g., any local minimum of an explicitly B-preinvex function is also a global one and the summation of two functions, which are both B-preinvex and explicitly B-preinvex, is also a B-preinvex function and an explicitly B-preinvex function. Furthermore, it is shown that the explicit B-preinvexity, together with the intermediate-point B-preinvexity, implies B-preinvexity, while the explicit B-preinvexity, together with a lower semicontinuity, implies the B-preinvexity

Higher-order generalized convexity and duality in nondifferentiable multiobjective mathematical programming

AbstractIn this paper, a class of generalized convexity is introduced and a unified higher-order dual model for nondifferentiable multiobjective programs is described, where every component of the objective function contains a term involving the support function of a compact convex set. Weak duality theorems are established under generalized convexity conditions. The well-known case of the support function in the form of square root of a positive semidefinite quadratic form and other special cases can be readily derived from our results