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AI based realtime task schedulers for multicore processor based low power biomedical devices for health care application

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multiMEDIA TOOLS AND APPLICATIONS 2022年第29期81卷 42079-42095页

作者： Prabhaker, M. Lordwin Cecil Ponnan, Suresh Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept ECE Chennai Tamil Nadu India

The bioinformatics data processing plays a vital role in low power biomedical devices. The functional domain of processing biological data is collection, execution, conversion, storing and distribution. So, there is an effective multiple objective real time task scheduling technique are required to provide better solution in this domain. This paper describes novel AI based multi-objective evolutionary algorithmic techniques such as multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA) and multi-objective messy genetic algorithm (MOMGA) for scheduling real time tasks to a multicore processor-based low power biomedical device used for health care application. These techniques improve the performance upon earlier reported system by considering multiple objectives such as, low power consumption (P), maximizing core utilization (U) and minimizing deadline miss-rate (delta). The novelty of this work is to achieve the schedulability of realtime tasks by computing the converging value of a series of task parameters such as execution time, release time, workload and arrival time. Finally, we investigated the performance parameters such as power consumption (P), deadline miss-rate (delta), and core utilization for the given architecture. The evaluation results show that the power consumption is reduced to about 5-8%, utilization of the core is increased about 10% to 40% and deadline miss-rate is comparatively minimized with conventional realtime scheduling approaches.

关键词： Bioinformatics Low power biomedical devices multicore architecture multi-objective evolutionary algorithms AI realtime task schedulers

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multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1240页

作者： de Mendizabal, Inaki Velez Basto-Fernandes, Vitor Ezpeleta, Enaitz Mendez, Jose R. Gomez-Meire, Silvana Zurutuza, Urko Mondragon Unibertsitatea Elect & Comp Dept Gipuzkoa Spain Univ Inst Lisbon ISTAR Inst Univ Lisboa ISCTE IUL IUL Lisbon Portugal Hosp Alvaro Cunqueiro Galicia Hlth Res Inst IIS Galicia Sur Bloque tecn SING Res Grp Pontevedra Spain Biomed Res Ctr CINBIO Lagoas Marcosende Vigo Spain Dept Comp Sci Univ Vigo Orense Spain

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information (e.g., bringing together the synsets "viagra", "ciallis", "levitra" and other representing similar drugs by using "virility drug" which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a multi-objective evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic -based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOE

关键词： Spam filtering Synset-based representation Semantic-based feature reduction multi-objective evolutionary algorithms

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Efficient multi-objective evolutionary neural architecture search for U-Nets with diamond atrous convolution and Transformer for medical image segmentation

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Ying, Weiqin Zheng, Qiaoqiao Wu, Yu Yang, Kaihao Zhou, Zekun Chen, Jiajun Ye, Zilin South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Guangdong Peoples R China

Deep encoder-decoder neural networks like U-Nets have made significant contributions to the development of computer vision applications such as image segmentation. Neural architecture search (NAS) has the potential to further automatically adjust the architectures of U-Nets for various medical image segmentation tasks. Most of the NAS techniques focus on optimizing segmentation accuracies of network architectures. In real-world medical image segmentation scenarios, two main challenges are poor medical image quality and diverse deployment devices with different computing capabilities. A large architecture designed only for the high segmentation accuracy is difficult to run on various deployment devices. To address these challenges, this paper proposes a multi-objective evolutionary neural architecture search method (CTU-NAS) for U-Nets with diamond atrous convolution and Transformer for medical image segmentation. A hybrid U-Net architecture (CTU-Net) with diamond atrous convolution and Transformer modules is designed as the supernet of CTU-NAS. Then a channel search strategy based on sorting and selection is applied to speed up the search for subnets by precisely selecting and training the most important channels more times. In addition, CTU-NAS employs a dual acceleration mechanism based on weight sharing and surrogate model to lower the cost of evaluations of subnets. CTU-NAS applies a multi-objective evolutionary algorithm to balance between the segmentation accuracy and the number of parameters. Experimental results on two medical segmentation datasets show that CTU-NAS is capable of quickly generating a group of excellent network architectures with different sizes and their performances also outperform or come close to those of the manually designed networks.

关键词： Medical image segmentation Neural architecture search multi-objective evolutionary algorithms Diamond atrous convolution Surrogate model

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Adaptively weighted decomposition based multi-objective evolutionary algorithm

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APPLIED INTELLIGENCE 2021年第6期51卷 3801-3823页

作者： Meghwani, Suraj S. Thakur, Manoj Vellore Inst Technol Sch Comp Sci & Engn SCOPE Vellore Tamil Nadu India Indian Inst Technol Sch Basic Sci Mandi Himachal Prades India

multi-objective evolutionary algorithm based on Decomposition (MOEA/D) decomposes a multi-objective problem into a number of scalar optimization problems using uniformly distributed weight vectors. However, uniformly distributed weight vectors do not guarantee uniformity of solutions on approximated Pareto-Front. This study proposes an adaptive strategy to modify these scalarizing weights after regular intervals by assessing the crowdedness of solutions using crowding distance operator. Experiments carried out over several benchmark problems with complex Pareto-Fronts show that such a strategy helps in improving the convergence and diversity of solutions on approximated Pareto-Front. Proposed algorithm also shows better performance when compared with other state-of-the-art multi-objective algorithms over most of the benchmark problems.

关键词： multi-objective evolutionary algorithms MOEA D algorithm Inverted generational distance Crowding distance

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An Investigation into Many-objective Optimization Problems: A Case Study of the Dial-a-Ride Problem

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IEEE LATIN AMERICA TRANSACTIONS 2022年第1期20卷 73-81页

作者： Viana, Renan J. dos S. Martins, Flavio V. Cruzeiro Wanner, Elizabeth F. CEFET MG Programa Pos Grad Modelagem Matemat & Computac Belo Horizonte MG Brazil CEFET MG Dept Comp Belo Horizonte MG Brazil

multi-objective optimization problems with more than three objectives are commonly referred to as many-objective optimization problems. Usually, this class of problem brings new and complex challenges to the current optimization methods, mainly maintaining the right balance between convergence and diversity. During the last years, various approaches have been proposed to solve many-objective problems. However, most existing experimental comparative studies are restricted to continuous problems. Few studies have encompassed the most recently proposed state-of-the-art approaches and made an experimental comparison applied to combinatorial optimization problems. Aiming to fill this gap, this paper presents a comparative analysis with eight algorithms covering various categories to solve a many-objective Dial-a-Ride problem. The results show that different observations can be made about the algorithms' behavior when using different test sets. Also, algorithms originally proposed to deal with problems with up to three objectives have overcome recently proposed ones.

关键词： Many-objective optimization dial-a-ride problem multi-objective evolutionary algorithms combinatorial optimization

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Diffusely Distributed Parallelization of MOEA/D with Edge Weight Vectors Sharing

Diffusely Distributed Parallelization of MOEA/D with Edge We...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Sato, Yuji Midtlyng, Mads Sato, Mikiko Hosei Univ Fac Comp & Informat Sci Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan

ISBN: (纸本)9798400701207

This paper proposes the partitioning method with edge weight vectors sharing for parallel distributed MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the weight vector in the objective function space, the T-neighborhood is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a partitioning method to share edge weight vectors among all partitions, and then assign other weight vectors uniformly to each partition. Using the constrained knapsack problem as a benchmark problem, we show that it is possible to eliminate the need for migration processing to correct the value of the ideal point and improve the problem that the T-neighborhood is divided, and the distribution of the solution becomes sparse.

关键词： MOEA/D parallel and distributed processing distributed memory environment multi-objective evolutionary algorithms

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A new evolutionism based self-adaptive multi-objective optimization method to predict software cost estimation

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SOFTWARE-PRACTICE & EXPERIENCE 2022年第8期52卷 1826-1848页

作者： Gouda, Sunil Kumar Mehta, Ashok Kumar Natl Inst Technol Dept Comp Sci & Engn Jamshedpur Bihar India

One of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta-heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism-based self-adaptive mutation operator to solve the multi-objective optimization problems. This approach addresses the issues that exist in multi-objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism-based self-adaptive mutation operator in a multi-objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non-dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi-objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi-objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error.

关键词： multi-objective differential evolution multi-objective evolutionary algorithms optimization software cost estimation

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Unsupervised text feature selection using NSGA II with Hill Climbing local search 27

Unsupervised text feature selection using NSGA II with Hill ...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Cornei, Laura Croitoru, Eugen Luchian, Henri Faculty of Computer Science Alexandru Ioan Cuza University 16 General Berthelot St. Iasi700483 Romania

This paper introduces a novel unsupervised text feature selection technique that combines the multi-objective evolutionary algorithm NSGA II with a local Hill Climbing based search. The objective functions in NSGA II are an adapted version of the Mean Absolute Difference criterion and the Holes projection pursuit index, both of which incorporate information regarding the cardinality of the selected feature subset. The local Hill Climbing procedure aims to further improve the solutions provided by NSGA II through elimination/replacement of redundant features. The redundancy level of a feature F in a chromosome is measured via the lexical and semantic similarities between F and the other features represented in the chromosome. Experiments conducted on three real-world textual subdatasets indicate that our proposed approach achieves very good results in comparison to four state-of-the-art evolutionary unsupervised feature selection techniques and to one classical feature extraction method with proven effectiveness (Principal Component Analysis). © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Hill Climbing multi-objective evolutionary algorithms NLP NSGA-II semantic similarity unsupervised text feature selection

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Towards a Pareto Front Shape Invariant multi-objective evolutionary Algorithm Using Pair-Potential Functions 1

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20th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Marquez-Vega, Luis A. Guillermo Falcon-Cardona, Jesus Covantes Osuna, Edgar Tecnol Monterrey Sch Engn & Sci Monterrey 64849 Nuevo Leon Mexico UAM Cuajimalpa Dept Appl Math & Syst Mexico City 05348 DF Mexico

ISBN: (数字)9783030898175

ISBN: (纸本)9783030898175;9783030898168

Reference sets generated with uniformly distributed weight vectors on a unit simplex are widely used by several multi-objective evolutionary algorithms (MOEAs). They have been employed to tackle multi-objective optimization problems (MOPs) with four or more objective functions, i.e., the so-called many-objective optimization problems. These MOEAs have shown a good performance on MOPs with regular Pareto front shapes, i.e., simplex-like shapes. However, it has been observed that in many cases, their performance degrades on MOPs with irregular Pareto front shapes. In this paper, we designed a new selection mechanism that aims to promote a Pareto front shape invariant performance of MOEAs that use weight vector-based reference sets. The newly proposed selection mechanism takes advantage of weight vector-based reference sets and seven pair-potential functions. It was embedded into the non-dominated sorting genetic algorithm III (NSGA-III) to increase its performance on MOPs with different Pareto front geometries. We use the DTLZ and DTLZ(-1) test problems to perform an empirical study about the usage of these pair-potential functions for this selection mechanism. Our experimental results show that the pair-potential functions can enhance the distribution of solutions obtained by weight vector-based MOEAs on MOPs with irregular Pareto front shapes. Also, the proposed selection mechanism permits maintaining the good performance of these MOEAs on MOPs with regular Pareto front shapes.

关键词： Reference sets multi-objective evolutionary algorithms Pareto front shape invariant performance Pair-potential functions

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Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期107卷 104531-104531页

作者： Jimenez, Fernando Sanchez, Gracia Palma, Jose Sciavicco, Guido Univ Murcia Dept Informat & Commun Engn Murcia Spain Univ Ferrara Dept Math & Comp Sci Ferrara Italy

The large amount of data that is produced today with new technologies is an impediment for machine learning algorithms to work correctly, both due to the memory requirements and the necessary execution times. That is why the processes of reducing both the quantity and the size of the data are increasingly important. One of these processes is the so-called instance selection. In this paper we propose three-objective constrained optimization models to formulate instance selection wrapper and filter methods (separately) for classification problems, which are solved with multi-objective evolutionary algorithms and multi-objective differential evolution. In the proposed instance selection wrapper method, an objective is added to the usual ones to minimize the generalization error of the classifier. The proposed instance selection filter method simultaneously optimizes the correlation, redundancy and consistency of the datasets. Instance retention constraints are imposed on optimization models to retain a maximum percentage of samples, established by the decision maker, in big data scenarios. The experiments have been designed to compare (1) the NSGA-II and MODE algorithms, (2) two- and three-objective optimization models, (3) two different constraint handling techniques, and (4) the proposed evolutionary approaches and other 12 non-evolutionary approaches used in literature. The proposed wrapper and filter instance selection methods have been used in a real-world business engineering application, and have also been validated using three public datasets to facilitate the replicability of the research results. The results of the experiments show the superiority of the three-objective constrained evolutionary techniques proposed in this paper over the non-evolutionary techniques and over the two-objective evolutionary approaches used in the literature.

关键词： multi-objective evolutionary algorithms multi-objective constrained optimization Instance selection Classification

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