An adaptation reference-point-based multiobjective evolutionary algorithm

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization problems (MOPs) affects the performance of MOEAs, especially reference point-based ones. This paper proposes a reference-point-based adaptive method to study the PF of MOPs according to the candidate solutions of the population. In addition, the proportion and angle function presented selects elites during environmental selection. Compared with five state-of-the-art MOEAs, the proposed algorithm shows highly competitive effectiveness on MOPs with six complex characteristics

A proportion-based selection scheme for multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multiobjective optimizations problems when the number of objectives increases due to the lack of sufficient selection pressure towards the Pareto front (PF). This poses a great challenge to the design of MOEAs. To cope with this problem, researchers have developed reference-point based methods, where some well-distributed points are produced to assist in maintaining good diversity in the optimization process. However, the convergence speed of the population may be severely affected during the searching procedure. This paper proposes a proportion-based selection scheme (denoted as PSS) to strengthen the convergence to the PF as well as maintain a good diversity of the population. Computational experiments have demonstrated that PSS is significantly better than three peer MOEAs on most test problems in terms of diversity and convergence

An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance

Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments

A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods

Hyperphosphatemia in chronic kidney disease exacerbates atherosclerosis via a mannosidases-mediated complex-type conversion of SCAP N-glycans

Blood phosphate levels are linked to atherosclerotic cardiovascular disease in patients with chronic kidney disease (CKD), but the molecular mechanisms remain unclear. Emerging studies indicate an involvement of hyperphosphatemia in CKD accelerated atherogenesis through disturbed cholesterol homeostasis. Here, we investigated a potential atherogenic role of high phosphate concentrations acting through aberrant activation of sterol regulatory element-binding protein (SREBP) and cleavage-activating protein (SCAP)-SREBP2 signaling in patients with CKD, hyperphosphatemic apolipoprotein E (ApoE) knockout mice, and cultured vascular smooth muscle cells. Hyperphosphatemia correlated positively with increased atherosclerotic cardiovascular disease risk in Chinese patients with CKD and severe atheromatous lesions in the aortas of ApoE knockout mice. Mice arteries had elevated SCAP levels with aberrantly activated SCAP-SREBP2 signaling. Excess phosphate in vitro raised the activity of α-mannosidase, resulting in delayed SCAP degradation through promoting complex-type conversion of SCAP N-glycans. The retention of SCAP enhanced transactivation of SREBP2 and expression of 3-hydroxy-3-methyl-glutaryl coenzyme A reductase, boosting intracellular cholesterol synthesis. Elevated α-mannosidase II activity was also observed in the aortas of ApoE knockout mice and the radial arteries of patients with uremia and hyperphosphatemia. High phosphate concentration in vitro elevated α-mannosidase II activity in the Golgi, enhanced complex-type conversion of SCAP N-glycans, thereby upregulating intracellular cholesterol synthesis. Thus, our studies explain how hyperphosphatemia independently accelerates atherosclerosis in CKD

A predictive strategy based on special points for evolutionary dynamic multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThere are some real-world problems in which multiple objectives conflict with each other and the objectives change with time. These problems require an optimization algorithm to track the moving Pareto front or Pareto set over time. In this paper, we propose a predictive strategy based on special points (SPPS) which consists of three mechanisms. The first one is that the non-dominated set is predicted directly by feed-forward center points, which can eliminate many useless individuals predicted by traditional prediction using feed-forward center points. The second one is that a special point set(such as boundary point, knee point, etc.) is introduced into the predicted population which can track Pareto front or Pareto set more accurately. The third one is the adaptive diversity maintenance mechanism based on boundary points and center points. The mechanism can introduce diverse individuals of the corresponding number according to the degree of difficulty of the problem to keep the diversity of the population. The number of these diverse individuals is strongly related to the center points. Then, they are generated evenly throughout the decision space between the boundary points. The proposed strategy is compared with the four other state-of-the-art strategies. The experimental results show that SPPS can do well for dynamic multi-objective optimization