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multi-step time series prediction intervals using neuroevolution

NEURAL COMPUTING & APPLICATIONS 2020年第13期32卷 8939-8953页

作者： Cortez, Paulo Pereira, Pedro Jose Mendes, Rui Univ Minho ALGORITMI Ctr Dept Informat Syst P-4804533 Guimaraes Portugal Univ Minho Ctr Biol Engn ALGORITMI Ctr Dept Informat P-4710057 Braga Portugal

multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower-upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage-width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.

关键词： Prediction intervals Time series multi-objective evolutionary algorithm multilayer perceptron evolutionary neural network

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Soil Recycling Among Construction Sites by Optimizing Schedule and Costs for Earthmoving

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2017年第2期16卷 439-446页

作者： Choi, Hyemi Park, Min-Ho Jeong, Dae-Min Kim, Ju-Hyung Hanyang Univ Dept Sustainable Architectural Engn Seoul South Korea Hanyang Univ Dept Architectural Engn 222 Wangsimni Ro Seoul South Korea

Recycling uncontaminated excavated construction soil is beneficial because it reduces the costs to abandon excess soil or obtain refill soil from a distant location while alleviating environmental burdens. For this reason, various methods and techniques to support on-site soil reuse have been explored. However, in order to increase the reuse rate, excavated soil should be recycled among different construction sites as well. As a prerequisite for reusing excess soil in this context, the construction schedules, type of soil, trading volume, and incurred costs must be coordinated. In order to consider all of these aspects, earthmoving among construction sites needs to be planned by means of multi-objective optimization. This paper aims to present a practical solution supporting inter-site soil trade by introducing a non-dominated sorting algorithm-II (NSGA-II), a type of multi-objective evolutionary algorithm (MOEA). A description of the optimization procedure is provided, and computational results are presented to prove the effectiveness of the selected method.

关键词： excavated soil solid waste management multi-objective evolutionary algorithm non-dominated sorting algorithm-II

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Gene Subset Selection for Leukemia Classification Using Microarray Data

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CURRENT BIOINFORMATICS 2019年第4期14卷 353-358页

作者： Fajila, Mohamed Nisper Fathima South Eastern Univ Sri Lanka Fac Appl Sci Dept Math Sci Sammanthurai Sri Lanka

Background: Cancer subtype identification is an active research field which helps in the diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. objective: Yet, a precise analysis is important in microarray data applications as microarray experiments provide huge amount of data. Gene selection techniques promote microarray usage in the field of medicine. The objective of gene selection is to select a small subset of genes, which are the most informative in classification. Method: In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multi-objective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. Results: The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach. Conclusion: Thus, it is obvious that the proposed study perfectly classifies Leukemia with only few significant genes.

关键词： Classification gene selection microarray multi-objective evolutionary algorithm redundant and irrelevant gene significant genes

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Adaptive arm motion generation of humanoid robot operating in dynamic environments

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2014年第2期41卷 124-134页

作者： Mohamed, Zulkifli Kitani, Mitsuki Capi, Genci Toyama Univ Dept Elect & Elect Syst Engn Toyama 930 Japan Univ Teknol MARA Fac Mech Engn Shah Alam Selangor Malaysia

Purpose - The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments. Design/methodology/approach - The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions;minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered. Findings - The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions. Research limitations/implications - The motion generated yield good results in both simulation and experimental environments. Practical implications - The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion. Originality/value - A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.

关键词： Arm motion generation Mobile humanoid robot multi-objective evolutionary algorithm

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A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 465卷 124-140页

作者： Yue, Wei Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xian Polytech Univ Sch Sci Xian 710071 Peoples R China

Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Portfolio selection Fuzzy variable Possibilistic moments Entropy multi-objective evolutionary algorithm

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multi-objective genetic local search algorithm using Kohonen's neural map

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COMPUTERS & INDUSTRIAL ENGINEERING 2009年第4期56卷 1566-1576页

作者： Hakimi-Asiabar, Mehrdad Ghodsypour, Seyyed Hassan Kerachian, Reza Amirkabir Univ Technonol Dept Ind Eng Tehran Iran Univ Tehran Ctr Excellence Infrastruct Engn & Management Fac Civil Engn Tehran Iran

Genetic algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAS is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations. In this paper, a new method called SOM-Based multi-objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the evolutionary algorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objectiv

关键词： multi-objective genetic local search Self organizing maps Variable Neighborhood Search (VNS) multi-objective evolutionary algorithm Learning multi-reservoir operation management

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Robust routing and channel allocation in multi-hop cognitive radio networks

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WIRELESS NETWORKS 2015年第1期21卷 127-137页

作者： Dong, Bei Wu, Jianshe Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Jointly consider routing and spectrum selection is essentially and necessary in multi-hop cognitive radio networks. System cost and throughput are commonly used to evaluate performance of routing and spectrum selection schemes. Traditional methods mostly translate these metrics into a single objective function, and corresponding weights are allocated to each metric representing impact on the entire network performance. Optimal solutions of these approaches are sensitive to the weight settings which are usually hard to appropriately chosen. In this work, the task of routing and channel allocation is modeled as a two-objective optimization problem. Two conflicting metric functions system total throughput and total cost are optimized simultaneously, and a novel memetic algorithm which adopts a new neighborhood search procedure is proposed to solve this problem. Incorporated with robustness consideration on routing, a new robustness metric is also presented to work as a decision mechanism to ensure the robustness of the entire network. The aim of this task is to find the best compromise routing and channel allocation scheme on system throughput, cost and robustness among the feasible solution set. Simulation results demonstrate that the optimal solution set obtained by the memetic algorithm can clearly show the conflicting relationship of the system cost and throughput when choosing different routing and channel selection schemes. The best solution made by the additional robustness metric among these solutions can achieve the best performance of the cognitive radio network.

关键词： Cognitive radio Cognitive radio networks Routing Robustness multi-objective evolutionary algorithm

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multi-objective resource allocation for Edge Cloud based robotic workflow in smart factory

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 97卷 119-130页

作者： Afrin, Mahbuba Jin, Jiong Rahman, Ashfaqur Tian, Yu-Chu Kulkarni, Ambarish Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia CSIRO Data61 Sandy Bay Tas 7005 Australia Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4001 Australia Swinburne Univ Technol Sch Engn Melbourne Vic 3122 Australia

multi-robotic services are widely used to enhance the efficiency of Industry 4.0 applications including emergency management in smart factory. The workflow of these robotic services consists of data hungry, delay sensitive and compute intensive tasks. Generally, robots are not enriched in computational power and storage capabilities. It is thus beneficial to leverage the available Cloud resources to complement robots for executing robotic workflows. When multiple robots and Cloud instances work in a collaborative manner, optimal resource allocation for the tasks of a robotic workflow becomes a challenging problem. The diverse energy consumption rate of both robot and Cloud instances, and the cost of executing robotic workflow in such a distributed manner further intensify the resource allocation problem. Since the tasks are inter-dependent, inconvenience in data exchange between local robots and remote Cloud also degrade the service quality. Therefore, in this paper, we address simultaneous optimization of makespan, energy consumption and cost while allocating resources for the tasks of a robotic workflow. As a use case, we consider resource allocation for the robotic workflow of emergency management service in smart factory. We design an Edge Cloud based multi-robot system to overcome the limitations of remote Cloud based system in exchanging delay sensitive data. The resource allocation for robotic workflow is modelled as a constrained multi-objective optimization problem and it is solved through a multi-objective evolutionary approach, namely, NSGA-II algorithm. We have redesigned the NSGA-II algorithm by defining a new chromosome structure, pre-sorted initial population and mutation operator. It is further augmented by selecting the minimum distant solution from the non-dominated front to the origin while crossing over the chromosomes. The experimental results based on synthetic workload demonstrate that our augmented NSGA-II algorithm outperforms the state-of-t

关键词： Resource allocation multi-robot system Edge cloud Workflow management Smart factory multi-objective evolutionary algorithm

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Shape optimization of an axial compressor blade by multi-objective genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2008年第A6期222卷 599-611页

作者： Samad, A. Kim, K-Y Inha Univ Dept Mech Engn Inchon 402751 South Korea

In this study, a multi-objective optimization of an axial compressor rotor blade has been performed through genetic algorithm with total pressure and adiabatic efficiency as objective functions. The non-dominated sorting of genetic algorithm-II has been implemented and confidence check has been performed at k-means clustered points among all the Pareto-optimal solutions. Reynolds-averaged Navier-Stokes equations are solved to obtain the objective function and flow field inside the compressor annulus. The objective functions are used to generate Pareto-optimal front. The design variables are selected from blade lean and thickness through the Bezier polynomial formulation. By this optimization, maximum efficiency and total pressure are increased by 1.76 and 0.41 per cent, respectively, when two extreme clustered points are considered as optimal designs.

关键词： non-dominated sorting of genetic algorithm-II multi-objective evolutionary algorithm axial compressor blade efficiency total pressure response surface method

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Joint sparse neural network compression via multi-application multi-objective optimization

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APPLIED INTELLIGENCE 2021年第11期51卷 7837-7854页

作者： Chen, Jinzhuo Xu, Yongnan Sun, Weize Huang, Lei Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen Key Lab Adv Nav Technol Coll Elect & Inf Shenzhen Guangdong Peoples R China

Over the pass decade, deep neural network (DNN) has been widely applied in various applications. To alleviate the storage and computation requirement of the complicated DNNs, network compression methods are developed. The sparse structure learning methods based on multi-objective optimization have been proven to be valid to balance the sparsity of the network model and network performance. However, when multiple applications are deployed on one single platform simultaneously, these methods become inefficient because each network model for each application needs to be trained and optimized individually. In this article, a multi-objective, multi-application sparse learning model is proposed to optimize multiple targets from a set of applications together. The joint network structure is first proposed. After a pre-training of the network model, a joint multi-objective evolutionary algorithm is derived to solve the optimization problems. Note that an improved initialization method for parent model generation is also developed. Finally, based on the joint loss between the objectives, fine tuning is used to compute the final models with good performance. The proposed method is evaluated under different datasets with a comparison to the state-of-the-art approaches, and experimental results demonstrate that the multi-application optimization model can give much better performance than the single-application optimization ones, especially in the case that different datasets are involved simultaneously.

关键词： Deep neural network Sparse network learning multi-objective evolutionary algorithm multi-objective optimization

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