A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

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.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention owing to its great potential in applications. Various manually designed multimodal deep networks have performed well in multimodal HGR (MHGR), but most of existing algorithms require a lot of expert experience and time-consuming manual trials. To address these issues, we propose an evolutionary network architecture search framework with the adaptive multimodel fusion (AMF-ENAS). Specifically, we design an encoding space that simultaneously considers fusion positions and ratios of the multimodal data, allowing for the automatic construction of multimodal networks with different architectures through decoding. Additionally, we consider three input streams corresponding to intra-modal surface electromyography (sEMG), intra-modal accelerometer (ACC), and inter-modal sEMG-ACC. To automatically adapt to various datasets, the ENAS framework is designed to automatically search a MHGR network with appropriate fusion positions and ratios. To the best of our knowledge, this is the first time that ENAS has been utilized in MHGR to tackle issues related to the fusion position and ratio of multimodal data. Experimental results demonstrate that AMF-ENAS achieves state-of-the-art performance on the Ninapro DB2, DB3, and DB7 datasets

A two-phase differential evolution for uniform designs in constrained experimental domains

open access articleIn many real-world engineering applications, a uniform design needs to be conducted in a constrained experimental domain that includes linear/nonlinear and inequality/equality constraints. In general, these constraints make the constrained experimental domain small and irregular in the decision space. Therefore, it is difficult for current methods to produce a predefined number of samples and make the samples distribute uniformly in the constrained experimental domain. This paper presents a two-phase differential evolution for uniform designs in constrained experimental domains. In the first phase, considering the constraint violation as the fitness function, a clustering differential evolution is proposed to guide the population toward the constrained experimental domain from different directions promptly. As a result, a predefined number of samples can be obtained in the constrained experimental domain. In the second phase, maximizing the minimum Euclidean distance among samples is treated as another fitness function. By optimizing this fitness function, the samples produced in the first phase can be scattered uniformly in the constrained experimental domain. The performance of the proposed method has been tested and compared with another state-of-the-art method. Experimental results suggest that our method is significantly better than the compared method in the uniform designs of a new type of automotive crash box and five benchmark test problems. Moreover, the proposed method could be considered as a general and promising framework for other uniform designs in constrained experimental domains

A novel discrete bat algorithm for heterogeneous redundancy allocation of multi-state systems subject to probabilistic common-cause failure

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.This paper focuses on a heterogeneous redundancy allocation problem (RAP) for multi-state series-parallel systems subject to probabilistic common-cause failure and proposes a novel discrete bat algorithm to solve it. Although abundant research studies have been published for solving multi-state RAPs, few of them have studied probabilistic common cause failure, which motivates this paper. Due to the insufficient data of components, an interval-valued universal generating function is utilized to evaluate the availability of components and the whole system. The challenge of solving this kind of RAPs lies in not only the reliability estimation, but also the solution method. This paper presents a novel discrete bat algorithm (BA) for effectively dealing with the proposed RAP and alleviating the premature convergence of BA. Two main features of the adaptation are Hamming distance-based bat movement (HDBM) and Q learning-based local search (QLLS). HDBM transfers the Hamming distance between the current bat and the best bat in the swarm to the movement rate. Then, QLLS utilizes Q-learning to adjust the local search strategies dynamically during the iterations. The computational results from extensive experiments demonstrate that the proposed algorithm is powerful, which is more efficient than other state-of-the-arts on this sort of problems

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

Evolutionary many-objective optimization：A survey

Many-objective optimization problems (MaOPs) widely exist in industrial and scientific fields, where there are more than 3 objectives that are conflicting with each other (i.e., the improvement of the performance in one objective may lead to the deterioration of the performance of some other objectives). Because of the conflict between objectives, there is no unique optimal solution for MaOPs, but a group of compromise solutions need to be obtained to balance between objectives. As a class of population-based optimization algorithms inspired by biological evolution principles evolutionary algorithms have been proved to be effective in solving MaOPs, and have become one of the research hot spots in the field of multi-objective optimization. In the past 20 years, the research on many-objective evolutionary algorithms (MaOEAs) has made great progress, and a large number of advanced evolutionary methods and evaluation systems have been proposed and improved. In this paper, the research progress of evolutionary many-objective optimization (EMaO) is comprehensively reviewed. Specifically, it includes: (1) Describing the relevant theoretical background of EMaO; (2) Analyzing the problems and challenges faced by evolutionary algorithms in solving MaOPs; (3) Discussing the development of MaOEAs in detail; (4) Summarizing MaOPs and performance indicators in detail; (5) Introducing the visualization tools for high-dimensional objective space; (6) Summarizing the application of MaOEAs in some fields, and (7) Providing suggestions for future research in the domain

An assessment of hepatitis E virus (HEV) in US blood donors and recipients: No detectable HEV RNA in 1939 donors tested and no evidence for HEV transmission to 362 prospectively followed recipients.

BACKGROUND: Hepatitis E virus (HEV) infection has become relevant to blood transfusion practice because isolated cases of blood transmission have been reported and because HEV has been found to cause chronic infection and severe liver disease in immunocompromised patients. STUDY DESIGN AND METHODS: We tested for immunoglobulin (Ig)G and IgM antibodies to the HEV and for HEV RNA in 1939 unselected volunteer US blood donors. Subsequently, we tested the same variables in pre- and serial posttransfusion samples from 362 prospectively followed blood recipients to assess transfusion risk. RESULTS: IgG anti-HEV seroprevalence in the total 1939 donations was 18.8%: 916 of these donations were made in 2006 at which time the seroprevalence was 21.8% and the remaining 1023 donations were in 2012 when the seroprevalence had decreased to 16.0% (p \u3c 0.01). A significant (p \u3c 0.001) stepwise increase in anti-HEV seroprevalence was seen with increasing age. Eight of 1939 donations (0.4%) tested anti-HEV IgM positive; no donation was HEV RNA positive. Two recipients had an apparent anti-HEV seroconversion, but temporal relationships and linked donor testing showed that these were not transfusion-transmitted HEV infections. CONCLUSION: No transfusion-transmitted HEV infections were observed in 362 prospectively followed blood recipients despite an anti-HEV seroprevalence among donations exceeding 16%

Automatically constructing a health indicator for lithium-ion battery state-of-health estimation via adversarial and compound staked autoencoder

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.Precisely assessing the state of health (SOH) has emerged as a critical approach to ensuring the safety and dependability of lithium-ion batteries. One of the primary issues faced by SOH estimate methods is their susceptibility to the influence of noise in the observed variables. Moreover, we prefer to automatically extract explicit features for data-driven methods in certain circumstances. In light of these considerations, this paper proposes an adversarial and compound stacked autoencoder for automatically constructing the SOH estimation health indicator. The compound stacked autoencoder consists of two parts. The first one is a denoising autoencoder that learns three different denoising behaviors. The second is a feature-extracting autoencoder that employs adversarial learning to improve generalization ability. The experimental results show that the proposed compound stacked autoencoder can not only get explainable explicit features but also can achieve accurate SOH estimation results compared with its rivals. In addition, the results with transfer learning demonstrate that the proposed method not only can provide high generalization ability but also be easily transferred to a new SOH estimation task

The analysis of lysine succinylation modification reveals the mechanism of oxybenzone damaging of pakchoi (Brassica rapa L. ssp. chinensis)

Oxybenzone (OBZ), one of a broad spectrum of ultraviolet (UV) absorbents, has been proven to be harmful to both plants and animals, while omics analysis of big data at the molecular level is still lacking. Lysine succinylation (Ksuc) is an important posttranslational modification of proteins that plays a crucial role in regulating the metabolic network in organisms under stress. Here, we report the changes in intracellular Ksuc modification in plants under OBZ stress. A total of 1276 succinylated sites on 507 proteins were identified. Among these sites, 181 modified proteins were hypersulfinylated/succinylated in OBZ-stressed pakchoi leaves. Differentially succinylated proteins (DSPs) are distributed mainly in the chloroplast, cytoplasm, and mitochondria and are distributed mainly in primary metabolic pathways, such as reactive oxygen species (ROS) scavenging, stress resistance, energy generation and transfer, photosynthetic carbon fixation, glycolysis, and the tricarboxylic acid (TCA) cycle. Comprehensive analysis shows that Ksuc mainly changes the carbon flow distribution, enhances the activity of the antioxidant system, affects the biosynthesis of amino acids, and increases the modification of histones. The results of this study first showed the profiling of the Kusc map under OBZ treatment and proposed the adaptive mechanism of pakchoi in response to pollutants and other abiotic stresses at the posttranslational level, which revealed the importance of Ksuc in the regulation of various life activities and provides a reference dataset for future research on molecular function

An exploration of hiking risk perception : dimensions and antecedent factors

Hiking is a form of green tourism which deserves promotion and popularization, especially in present day China. However, the risks inherent in hiking could have a negative impact on the development of hiking tourism. It is important to better understand how people perceive the risks of hiking and what type of experience attributes they prefer. However, no studies have investigated the nature of risk perception from the perspective of hikers. This study explores the dimensions of the perceived risk of hiking and investigates the associated factors of hiking risk perception as well as hiking preference. A questionnaire with 18 items was used to capture people’s perception of hiking risks, and two groups of samples were surveyed. Generally, this study identified two dimensions of perceived risk towards hiking based on a sample of hikers, i.e., physical risk and psychological risk. Demographic variables such as gender, upbringing background, and hiking frequency were shown to predict hiking risk perception while gender and hiking frequency predicted route preference. The personality trait of sensation seeking appeared to be a significant predictor of hiking preference. These findings lend themselves to market segmentation and marketing strategies on hiking tourism