A Multi-Agent Evolutionary algorIthm for Connector-Based Assembly Sequence Planning

AbstractSome Evolutionary algorithms for connector-based ASP have been researched. But those algorithms have lots of blind searching because individuals have little intelligence in making use of geometry and assembly process information of product assembly body. To improve individuals’ intelligence, A multi-agent evolutionary algorithm for connector-based ASP (MAEA-ASP) is presented which is integrated with the multi-agent systems. learning, competition and crossover -mutation are designed as the behaviors of agent which locate lattice-like structure environment. Experimental results show that MAEA-ASP can find an approximate solution faster compared with other evolutionary algorithms

Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm

Knowledge graph embedding by dynamic translation

Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the performance of knowledge acquisition, fusion and inference. Among various embedding models appeared in recent years, the translation-based models such as TransE, TransH, TransR and TranSparse achieve state-of-the-art performance. However, the translation principle applied in these models is too strict and can not deal with complex entities and relations very well. In this paper, by introducing parameter vectors into the translation principle which treats each relation as a translation from the head entity to the tail entity, we propose a novel dynamic translation principle which supports flexible translation between the embeddings of entities and relations. We use this principle to improve the TransE, TransR and TranSparse models respectively and build new models named TransE-DT, TransR-DT and TranSparse-DT correspondingly. Experimental results show that our dynamic translation principle achieves great improvement in both the link prediction task and the triple classification task

SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT

Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for language tasks are too simplistic, and deep architectures have not been fully explored, resulting in a significant performance gap compared to mainstream transformer-based networks such as BERT. To this end, we improve a recently-proposed spiking transformer (i.e., Spikformer) to make it possible to process language tasks and propose a two-stage knowledge distillation method for training it, which combines pre-training by distilling knowledge from BERT with a large collection of unlabelled texts and fine-tuning with task-specific instances via knowledge distillation again from the BERT fine-tuned on the same training examples. Through extensive experimentation, we show that the models trained with our method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve comparable results to BERTs on text classification tasks for both English and Chinese with much less energy consumption

Radiocarbon Dating of Charcoal and Bone Collagen Associated with Early Pottery at Yuchanyan Cave, Hunan Province, China

Yuchanyan Cave in Daoxian County, Hunan Province (People's Republic of China), yielded fragmentary remains of 2 or more ceramic vessels, in addition to large amounts of ash, a rich animal bone assemblage, cobble and flake artifacts, bone tools, and shell tools. The artifacts indicate that the cave was a Late Paleolithic foragers' camp. Here we report on the radiocarbon ages of the sediments based on analyses of charcoal and bone collagen. The best-preserved charcoal and bone samples were identified by prescreening in the field and laboratory. The dates range from around 21,000 to 13,800 cal BP. We show that the age of the ancient pottery ranges between 18,300 and 15,430 cal BP. Charcoal and bone collagen samples located above and below one of the fragments produced dates of around 18,000. These ceramic potsherds therefore provide some of the earliest evidence for pottery making in China.Anthropolog

Segment-based predominant learning swarm optimizer for large-scale optimization

Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning from different exemplars while the ones in the same segment are evolved by the same exemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSO evolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified

Implementing a Process Model for Real-Time Applications

Model-based advanced control of industrial processes requires well-developed process models. The established models are often complex with the features of multi-dimension, high nonlinearity, strong coupling, etc. Those features make the computational demand of the models conceptually high, especially for large-scale systems, resulting in difficulties in implementation of real-time control systems. This paper reports our efforts in overcoming the difficulties for a large-scale industrial process: a continuous hot dip galvanising production line. The process model is developed first, which is a set of partial differential equations. It is then transformed into a discrete state space model suitable for computation using digital computers. In order to meet the requirements of real-time control, algorithms are designed to significantly reduce the computational time. The process model and its computational algorithms are implemented and embedded into the real-time control system of the galvanising production line in the real plant

Continuous Petri Nets Augmented With Maximal And Minimal Firing Speeds

CPNs has been a useful tool not only to approximate a discrete system but also to model a continuous process. In this paper, CPNs are augmented with maximal and minimal firing speeds, and Interval speed CPNs (ICPNs) is defined. The enabling and firing of transitions of ICPNs are discussed, and the enabling of continuous transitions is classified into three levels: 0-level, 1-level and 2-level. Some rules to calculate the instantaneous firing speeds are also developed. In addition, illustrative examples are presented