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multi-objective evolutionary design of central pattern generator network for biomimetic robotic fish

COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1707-1727页

作者： Li, Wei Kun Chen, Hao Cui, Wei Cheng Song, Chang Hui Chen, Lin Ke Westlake Inst Adv Study Inst Adv Technol 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Zhejiang Univ Zhejiang Univ Westlake Univ Joint Training Hangzhou 310024 Peoples R China Westlake Univ Sch Engn 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Key Lab Coastal Environm & Resources Zhejiang Pro 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China

Fish-inspired motion is an important research area with many applications in real-world tasks such as underwater vehicles or robotic fish control design. Owing to robust, smooth, and coordinated oscillatory signals generated by Central Pattern Generators (CPGs) for locomotion control of robots with multiple degrees of freedom, CPGs are the most versatile solution for robotic control systems, especially in robotic fish. However, tuning central pattern generator parameters is difficult for complex mechanical system designs. Besides, most current CPG-based methods only consider one aspect (e.g., speed), which widens the gap between theory and practice in robotic fish design. Also, it may affect the practical applicability of the designed motion model to a certain extent. This paper addresses this problem by constructing a multi-objective evolutionary design of a central pattern generator network to control the proposed biomimetic robotic fish. A new CPG model is proposed to help biomimetic robotic fish swim efficiently. In addition, an efficient multi-objective evolutionary algorithm proposed in our previous work is also applied to assist the biomimetic robotic fish in obtaining faster-swimming speed, good stability of the head, and higher propulsive efficiency simultaneously. Considering that the result of multi-objective optimization is a set of non-dominated solutions rather than a solution, a screening method based on fuzzy theory is adopted to assist decision-makers in selecting the most appropriate solution. Based on this, the control model of biomimetic robotic fish is constructed. The proposed control model is simulated and compared with seven well-known algorithms and a series of robotic fish designs. After that, the proposed control model is validated with extensive experiments on the actual biomimetic robotic fish. Simulations and experiments demonstrate the proposed control model's effectiveness and good performance, especially when the control model has be

关键词： Central pattern generators Biomimetic robotic fish multi-objective evolutionary algorithm Robotic fish control model

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evolutionary algorithm based Ensemble Extractive Summarization for Developing Smart Medical System

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2021年第2期13卷 229-259页

作者： Mallick, Chirantana Das, Asit Kumar Nayak, Janmenjoy Pelusi, Danilo Vimal, S. Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur 711103 Howrah India Aditya Inst Technol & Management AITAM Dept Comp Sci & Engn Tekkali 532201 Andhra Pradesh India Univ Teramo Dept Commun Sci Teramo Italy Natl Engn Coll Dept Informat Technol Kovilpatti 628503 Tamil Nadu India

The amount of information in the scientific literature of the bio-medical domain is growing exponentially, which makes it difficult in developing a smart medical system. Summarization techniques help for efficient searching and understanding of relevant information from the medical documents. In the paper, an evolutionary algorithm based ensemble extractive summarization technique is devised as a smart medical application with the idea of hybrid artificial intelligence on natural language processing. We have considered the abstracts of the target article and its cited articles as the base summaries and a multi-objective evolutionary algorithm is applied for generating the ensemble summary of the target article. Each sentence of the base summaries is represented by a concept vector of the medical terms contained in it with the help of the Unified Modelling Language System (UMLS) tool which is widely used in various smart medical applications. These terms carry the key information of the sentence which is very useful to find out the semantic similarity among the sentences. Fitness functions of the evolutionary algorithm are mainly defined using clustering coefficient and sparsity index, the concepts of graph theory. After the convergence of the algorithm, the best solution of the final population gives the ensemble summary. Next, the semantic similarity of each sentence in the target article with the ensemble summary is calculated and the sentences which are most similar to the ensemble summary are considered as the summary of the target article. The method is applied to the articles available in the PubMed MEDLINE database system and experimental results are compared with some state of the art methods applied in the Bio-medical domain. Experimental results and comparative study based on the performance evaluation show that the method competes with some recently proposed summarization methods and outperforms others, which express the effectiveness of the proposed meth

关键词： Bio-Medical informatics Ensemble summary Supervised extractive summarization multi-objective evolutionary algorithm Clustering coefficient Sparsity index

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A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems

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ANNALS OF OPERATIONS RESEARCH 2011年第1期186卷 213-229页

作者： Liao, Shu-Hsien Hsieh, Chia-Lin Lin, Yu-Siang Tamkang Univ Dept Management Sci & Decis Making Taipei 251 Taiwan Natl Taiwan Univ Sci & Technol Taiwan Tech Dept Ind Management Taipei 106 Taiwan

In this paper, we propose an integrated model to incorporate inventory control decisions-such as economic order quantity, safety stock and inventory replenishment decisions-into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk poling phenomenon. multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (responsive level). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single measure approaches. A multi-objective location-inventory model which permits a comprehensive trade-off evaluation for multi-objective optimization is initially presented. More specifically, a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio and inventory control parameters in order to reach best compromise of these conflicting criteria. An experimental study using practical data was then illustrated for the possibility of the proposed approach. Computational results have presented promising solutions in solving a practical-size problem with 50 buyers and 15 potential DCs and proved to be an innovative and efficient approach for so called difficult-to-solve problems.

关键词： Supply chain integration Vendor-managed inventory multi-objective supply chain model multi-objective evolutionary algorithm

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A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm

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ANNALS OF OPERATIONS RESEARCH 2013年第1期206卷 449-483页

作者： Tavana, Madjid Khalili-Damghani, Kaveh Abtahi, Amir-Reza La Salle Univ Lindback Distinguished Chair Informat Syst & Deci Philadelphia PA 19141 USA Islamic Azad Univ Dept Ind Engn South Tehran Branch Tehran Iran Univ Econ Sci Fac Econ Inst Management Dept Knowledge Engn & Decis Sci Tehran Iran

Project portfolio selection problems are inherently complex problems with multiple and often conflicting objectives. Numerous analytical techniques ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve these problems with precise data. However, the project data in real-world problems are often imprecise or ambiguous. We propose a fuzzy multidimensional multiple-choice Knapsack Problem (MMKP) formulation for project portfolio selection. The proposed model is composed of an Efficient Epsilon-Constraint (EEC) method and a customized multi-objective evolutionary algorithm. A Data Envelopment Analysis (DEA) model is used to prune the generated solutions into a limited and manageable set of implementable alternatives. Statistical analysis is performed to investigate the effectiveness of the proposed approach in comparison with the competing methods in the literature. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

关键词： multidimensional multiple-choice knapsack multi-objective evolutionary algorithm Efficient epsilon-constraint Data envelopment analysis

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multi-objective evolutionary Instance Selection for multi-label Classification 19th

Multi-objective Evolutionary Instance Selection for Multi-la...

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Liu, Dingming Shang, Haopu Hong, Wenjing Qian, Chao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031208614;9783031208621

multi-label classification is an important topic in machine learning, where each instance can be classified into more than one category, i.e., have a subset of labels instead of only one. Among existing methods, ML-kNN [25], the direct extension of k-nearest neighbors algorithm to the multi-label scenario, has received much attention due to its conciseness, great interpretability, and good performance. However, ML-kNN usually suffers from a terrible storage cost since all training instances need to be saved in the memory. To address this issue, a natural way is instance selection, intending to save the important instances while deleting the redundant ones. However, previous instance selection methods mainly focus on the single-label scenario, which may have a poor performance when adapted to the multi-label scenario. Recently, few works begin to consider the multi-label scenario, but their performance is limited due to the inapposite modeling. In this paper, we propose to formulate the instance selection problem for ML-kNN as a natural bi-objective optimization problem that considers the accuracy and the number of retained instances simultaneously, and adapt NSGA-II to solve it. Experiments on six real-world data sets show that our proposed method can achieve both not worse prediction accuracy and significantly better compression ratio, compared with state-of-the-art methods.

关键词： multi-label classification ML-kNN Instance selection multi-objective optimization multi-objective evolutionary algorithm

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A multi-objective Covariance Matrix Adaptation evolutionary Strategy based on Decomposition for Analog Circuit Design

A Multi-Objective Covariance Matrix Adaptation Evolutionary ...

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39th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)

作者： Park, So-Youn Lee, Ju-Jang Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

ISBN: (纸本)9781479902248

Sound localization has been utilized in many fields. For good performance, sound signal obtained from the microphone has to be well filtered and amplified. Although filtering and amplification can be implemented in digital, analog circuit is needed because of sampling aliasing problem and limitation of computational resources in real-time application. The analog circuit consists of several components. Because all of possible combinations cannot be investigated, evolutionary algorithm (EA) is chosen to find promising solution candidates. In the paper, we built a multi-objective covariance adaptation evolutionary strategy based on decomposition to obtain the component values. Through the experimental results, our proposed algorithm showed good performance.

关键词： State variable filter covariance matrix adaptation evolution strategy voltage level shifter circuit optimization multi-objective evolutionary algorithm scalarization

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Search Process Analysis of multiobjective evolutionary algorithms using Convergence-Diversity Diagram 12

Search Process Analysis of Multiobjective Evolutionary Algor...

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Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Kinoshita, Takato Masuyama, Naoki Nojima, Yusuke Osaka Prefecture Univ Guraduate Sch Engn Sakai Osaka Japan Osaka Metropolitan Univ Grad Sch Informat Sakai Osaka Japan

ISBN: (纸本)9781665499248

Many tasks in the real world are multi-objective optimization problems (MOPs). Population-based approaches are promising for solving MOPs. In particular, multi-objective evolutionary algorithms (MOEAs) are popular and have been actively studied over the last two decades. However, since it is not easy to directly display and compare multi-dimensional solution sets, it is difficult to analyze the search process of MOEAs using direct visualization techniques such as scatter plots. This paper proposes an analytical method to compare multiple search processes in terms of convergence and diversity by extending the authors' previous work, i.e., Convergence-Diversity Diagram. Through computational experiments, the proposed method reveals characteristics and similarities in three representative MOEAs and six test problems. In addition, this paper provides discussions on algorithm design, biases in the DTLZ test suite, and the improvement of visualization based on experimental results.

关键词： multi-objective optimization multi-objective evolutionary algorithm search process analysis convergence diversity

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Sensor Event Mining with Hybrid Ensemble Learning and evolutionary Feature Subset Selection Model 3

Sensor Event Mining with Hybrid Ensemble Learning and Evolut...

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IEEE International Conference on Big Data

作者： Mehdiyev, Nijat Krumeich, Julian Werth, Dirk Loos, Peter German Res Ctr Artificial Intelligence DFKI Inst Informat Syst IWi Saarbrucken Germany

ISBN: (纸本)9781479999255

Recent advancements in sensor technology offer opportunities to manage business processes in a proactive manner. To enable an effective and real-time monitoring, sensor data have to be treated and processed in an event processing manner. Complex Event Processing is an efficient technology that detects useful complex events by matching primitive sensor events using event patterns. Event patterns can be represented as templates that combine primitive events by temporal, logical, spatial and sequential correlations to detect more complex events. Identifying event patterns out of streaming data with a high data volume and velocity is a challenging task. In this paper, we propose an Ensemble Model consisting of a crisp and fuzzy rule based classifiers in order to derive decision rules as event patterns. Before implementing the ensemble classifier directly to the streaming data, we select the most influential feature subset using a multi-objective evolutionary algorithm. The performance of the proposed model was evaluated using real data obtained from accelerometer sensors. Promising results with high accuracy and appropriate level of computational complexity were obtained and discussed.

关键词： multi-objective evolutionary algorithm feature subset selection rule induction complex event processing ensemble learning

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An evolutionary algorithm-Based Text Categorization Technique 4th

An Evolutionary Algorithm-Based Text Categorization Techniqu...

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4th International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Das, Ajit Kumar Das, Asit Kumar Sarkar, Apurba Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Howrah 711103 W Bengal India

ISBN: (纸本)9789811080555;9789811080548

In general, most of the organizations generate unstructured data from which extraction of meaningful information becomes a difficult task. Preprocessing of unstructured data before mining helps to improve the efficiency of the mining algorithms. In this paper, text data is initially preprocessed using tokenization, stop word removal, and stemming operations and a bag-of-words is identified to characterize the text dataset. Next, improved strength pareto evolutionary algorithm-based genetic algorithm is applied to determine the more compact set of informative words for clustering of text documents efficiently. It is a bi-objective genetic algorithm used to approximate the pareto-optimal front exploring the search space for optimal solution. The external clustering index and number of words described in the documents are considered as two objective functions of the algorithm, and based on these functions chromosomes in the population are evaluated and the best chromosome in non dominated pareto front of final population gives the optimal set of words sufficient for categorizartion of text dataset.

关键词： Text mining Feature selection Text clustering Cluster validation multi-objective evolutionary algorithm

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evolutionary algorithms for a Three-objectives Oil Derivatives Network Problem

Evolutionary Algorithms for a Three-objectives Oil Derivativ...

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IEEE Congress on evolutionary Computation

作者： de Souza, Thatiana C. N. Goldbarg, Elizabeth F. G. Goldbarg, Marco C. Univ Fed Rio Grande do Norte Dept Informat & Appl Math Natal RN Brazil

ISBN: (纸本)9781479904549;9781479904532

To distribute oil derivatives by multi-product pipelines is an important problem faced by the petroleum industry. Some researchers deal with it as a discrete problem where batches of products flow in a network. Minimizing delivery time is a usual objective handled by engineers when scheduling products in pipeline networks. Nevertheless, other costs may also be considered such as losses due to interfaces and electrical energy. Losses due to interfaces occur when different products sent consecutively contaminate each other. Usually, no separation devices exist between batches of different products and losses due to interface can be significant. The price paid for electrical energy varies during the day, so it is important also to try to minimize this cost. These three minimization objectives are considered, simultaneously, i.e. delivery time at demand nodes, interface losses and electrical energy cost. A transgenetic algorithm is proposed and applied to thirty random instances. The results obtained with the proposed method are compared with those produced by an NSGAII algorithm.

关键词： distribution network oil derivatives multi-objective evolutionary algorithm

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