During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in literature. As pointed out some recent studies, however, performance an MOEA can strongly depend on Pareto front shape problem to be solved, whereas most existing MOEAs show poor versatility problems with different shapes fronts. To address this issue, we propose based enhanced inverted generational distance indicator, which adaptation method is suggested adjust set reference points...

Abstract Hyperspectral imaging technique was employed to determine spatial distributions of chlorophyll ( Chl ) and carotenoid Car contents in cucumber leaves response angular leaf spot (ALS). Altogether, 196 hyperspectral images with five infection severities ALS were captured by a system the range 380–1,030 nm covering 512 wavebands. Mean spectrum extracted from regions interest (ROIs) images. Partial least square regression (PLSR) models used develop quantitative analysis between spectra...

In real-world applications, there exist a lot of multiobjective optimization problems whose Pareto-optimal solutions are sparse, that is, most variables these 0. Generally, many sparse (SMOPs) contain large number variables, which pose grand challenges for evolutionary algorithms to find the optimal efficiently. To address curse dimensionality, this article proposes an algorithm solving large-scale SMOPs, aims mine distribution and, thus, considerably reduces search space. More specifically,...

In recent years, multiobjective evolutionary algorithms (MOEAs) have been demonstrated to show promising performance in feature selection (FS) tasks. However, designing an MOEA for high-dimensional FS is more challenging due the curse of dimensionality. To address this problem, article, a steering-matrix-based algorithm, called SM-MOEA, proposed. steering matrix suggested and harnessed guide evolution population, which not only improves search efficiency greatly but also obtains subsets with...

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection (FS). However, most existing EA-based FS methods, one bit individual only represents feature, which means with number features increasing, search space these methods increases exponentially and makes them not suitable for data classification high dimensions. To tackle issue, this article, a variable granularity search-based multiobjective EA, termed as VGS-MOEA, is proposed...

Task-oriented pattern mining is to find the most popular and complete for task-oriented applications such as goods match recommendation print area recommendation. In these applications, measure support used capture popularity of patterns, while occupancy adopted completeness patterns. Existing methods patterns usually combine two measures one optimization, require users set prior parameters minimum threshold min_sup, min_occ relative importance preference λ between occupancy. However, it...

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard be obtained by simple observations. Existing community algorithms have demonstrated their effectiveness on a variety networks, them, however, suffer from scalability issue without clear structure due challenge ambiguous structure. To address this issue, paper, we propose enhancement method, termed CSE, for networks. In proposed network...

Graph Convolutional Networks (GCNs) are widely used for skeleton-based action recognition and achieved remarkable performance. Due to the locality of graph convolution, GCNs can only utilize short-range node dependencies but fail model long-range relationships. In addition, existing convolution based methods normally use a uniform skeleton topology all frames, which limits ability feature learning. To address these issues, we present Convolution Network with Self-Attention (SelfGCN),...

The critical node detection based on cascade model is a very important way for analyzing network vulnerability and has recently attracted the attention of many researchers in complex area. Most existing works aim to design effective attack strategies which lead maximal damage (i.e. destructiveness attack), while number initial attacked nodes k) should be given by decision makers advance. In this paper, we transform as bi-objective optimization problem (named BCVND), where cost are optimized...

Given a fixed total budget and predefined cost model, the budgeted influence maximization problem aims to find subset of nodes maximize spread in social networks while its should be no more than budget. In this paper, we propose local-global indicator based constrained evolutionary algorithm, named IICEA, solve effectively efficiently. novel is firstly designed by considering two components: local neighbor information global community information, which can used better measure networks....

Multi-objective evolutionary algorithms (MOEAs) have been proved to be competitive for solving the critical node detection problem (CNDP) on complex networks in both non-cascading scenario and cascading scenario. With continuous expansion of network scale, search space existing MOEAs will increase exponentially. To this end, we propose an interactive co-evolutionary framework (ICoEF) solve multi-objective large-scale networks, where a set local populations as well global population are...

This article proposes a multi-agent system with predefined-time consensus to solve distributed optimization problem subject inconsistent constraint sets. Under some derived conditions, the of is guaranteed. Meanwhile, states agents are convergent an optimal solution constrained optimization. Compared finite time consensus, proposed has fixed that can be estimated in advance. The suggested for not requires objective functions have specific form and smooth. It suitable bounded sub-differential...

Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective (MOEAs) have shown their competitiveness in addressing the problem ROCCH maximization. Thus, series MOEAs with promising performance been proposed to tackle it. However, designing MOEA for high-dimensional ROOCH maximization much...

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection. However, limited by encoding scheme, most them face challenge "curse dimensionality". To address issue, this paper, an objective space constraint-based evolutionary algorithm, named OSC-EA, is proposed for high-dimensional selection (HDFS). Although decision EAs HDFS very huge, its same as that low-dimensional Based on fact, firstly modeled a constrained problem, where constraint...

Abstract Community detection problem in networks has received a great deal of attention during the past decade. Most community algorithms took into account only positive links, but they are not suitable for signed networks. In our work, we propose an algorithm based on random walks Firstly, local maximum degree node which larger compared with its neighbors is identified, and initial communities detected nodes. Then, calculate probability to be attracted by links walks, as well away from...

P2P lending is an increasingly prosperous financial market, where lenders can directly bid and invest on the loans posted by borrowers. However, when facing massive loan requests, it very difficult also boring for to choose portfolios meeting their ideal expectations. Actually, choosing loans, most pursue highest profit with lowest risk as well satisfying hobbies. In this article, we formalize a multi-objective optimization problem help select portfolios. Specifically, recommending scenario...

In recent decades, recommender systems have been well studied and widely applied. However, most recommenders unilaterally optimize the results from buying customers' views without considering expectations of other participants, e.g., merchants. Unfortunately, customers merchants in recommendation are different or even conflicted. Especially for popular group-trading markets, competing trading, i.e., want to meet their preferences obtain gains with personal favorite items, while recommend...