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16th IFAC Symposium on Information Control problems in Manufacturing (INCOM)

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal Univ Technol Troyes CNRS UMR 6281 ICDLASMIS Troyes France EPF Engn Sch Rosiere Pres Troyes France

This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimization problem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used. Finally, a case study highlighted the reliability of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization problem Fused Deposition Modeling Additive Manufacturing Non-Dominated Sorting Genetic Algorithm-II Stochastic Algorithm

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Hybrid heuristic search method for design of digital IIR filter with conflicting objectives

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SOFT COMPUTING 2017年第12期21卷 3461-3476页

作者： Sidhu, D. S. Dhillon, J. S. Kaur, Dalveer Dept Elect & Commun Engn Giani Zail Singh Punjab Tech Univ Campus Bathinda 151001 India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 India Punjab Inst Technol Dept Elect & Commun Engn Kapurthala 144601 India

The design of digital IIR filter design by using evolutionary algorithms has gained much attention in the previous years. Most of the researchers treated the design problem as a single objective optimization problem and applied the techniques for minimizing the magnitude response error. In this paper the design of filter is treated as a multi-objective problem by simultaneously minimizing the magnitude response error, linear phase response error and optimal order along with meeting the stability criterion. A hybrid heuristic search technique having differential evolution (DE) method as a global search technique and binary successive approximation based evolutionary search method as a local search technique has been proposed. Based on mean value of population, new mutation strategies have been proposed. The above proposed hybrid heuristic search technique has been applied effectively to solve the multi-parameter and multi-objective optimization problem of low-pass, high-pass, band-pass and band-stop digital IIR filter design. The obtained results reveal that the proposed technique with new proposed mutation strategies performs better than the already existing mutation strategies of DE and other algorithms applied by other researchers for the design of digital IIR filter.

关键词： Hybrid heuristic search technique Mutation strategies of DE Binary successive approximation (BSA) Digital IIR filter multi-objective optimization problem

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multi-objective optimization ON SUPERCRITICAL CO₂ RECOMPRESSION BRAYTON CYCLE USING KRIGING SURROGATE MODEL

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THERMAL SCIENCE 2017年第Sup1期21卷 S309-S316页

作者： Sun, Lei Wang, Chongyu Zhang, Di Xi An Jiao Tong Univ Sch Energy & Power Engn MOE Key Lab Thermofluid Sci & Engn Xian Shaanxi Peoples R China

Supercritical CO2 cycle has become one of the most popular research fields of thermal science. The selection of operation parameters on thermodynamic cycle process is an important task. The computational model of supercritical CO2 recompression cycle is built to solve the multi-objective problem in this paper. Then, the optimization of parameters is performed based on genetic algorithm. Several Kriging models are also used to reduce the quantity of samples. According to the calculation, the influence of sample quantity on the result and the time cost is obtained The results show that it is required to improve the heat transfer when improvement of the cycle efficiency is desired.

关键词： supercritical CO2 Brayton cycle recompression multi-objective optimization problem Kriging surrogate model

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A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 1235-1256页

作者： Rizk-Allah, Rizk M. El-Sehiemy, Ragab A. Deb, Suash Wang, Gai-Ge Univ Minoufia Dept Basic Engn Sci Shibin Al Kawm Egypt Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafr Al Sheikh Egypt Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Peoples R China

This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the first feature, a population of random fruit flies initializes the algorithm. During this initialization phase, the dominated fruit fly is replaced by the nearest non-dominated one. Subsequently, the fruit flies undergo evolution by flying randomly around the non-dominated solution or around the reference point, i.e., the best location of the individual objectives. Afterwards, the fruit flies are updated according to the nearest location whether from the reference point or the previous non-dominated location. For the second feature, the weighted sum method is incorporated to update the previous best locations of fruit flies and the reference point to emphasize the convergence of the non-dominated solutions. To prove the capability of the proposed MOFOA, two standard benchmark problems in addition to the real world application, namely, multi-objective shape design of tubular linear synchronous motor (TLSM) are checked. The corresponding TLSM objective functions aims to maximize operating force and to minimize the flux saturation. The outcomes clearly demonstrate the effectiveness of the proposed algorithm for finding the non-dominated solutions.

关键词： Fruit fly optimization algorithm multi-objective optimization problem Pareto-optimal solutions Reference point Tubular linear synchronous motor

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A new multiobjective evolutionary optimization algorithm based on θ-multiobjective clonal selection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017年第3期32卷 1685-1696页

作者： Zareizadeh, Zahra Helfroush, Mohammad Sadegh Kazemi, Kamran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In this paper a new multi-objective clonal selection algorithm (theta-MCSA) is presented to solve multi-objective problems with multimodal and non-continuous functions. The concept of clonal selection algorithm (CSA) is based on the immune system and white blood cells behavior that select the antibodies similar to antigen for cloning. Although the clonal selection is a robust optimization method, however, as a shortcoming, it takes long time to find optimal Pareto front especially in problems with large search space. To overcome this problem, the proposed method replaces the large search space with the theta-search based on the phase angles. To avoid trapping into local optima in mutation step, two strong mutation methods are implemented according to the iteration number and algorithm efficiency. For converging to uniformly Pareto front in less iterations, the proposed multi-objective algorithm handles the size of the repository and a new population updating mechanism is iteratively applied to select the non-dominate, one-dominate and two-dominate solutions of prior iteration. The experimental results show the efficiency of the proposed theta-MCSA algorithm compared to other methods.

关键词： Clonal selection multi-objective optimization problem theta-search method niching non-dominate solution

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A Surrogate-assisted Memetic Algorithm for Interval multi-objective optimization

A Surrogate-assisted Memetic Algorithm for Interval Multi-ob...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Sun, Jing Miao, Zhuang Gong, Dunwei Huaihai Inst Technol Sch Sci Lianyungang Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9781538627266

Interval multi-objective optimization problems (IMOPs) are ubiquitous and challenging. There are many optimizers for solving them;however, their drawbacks, such as the high computational cost and big uncertainty of the final front, hinder their applications in real-world situation. This paper proposes a surrogate-assisted interval multi-objective memetic algorithm (SA-IMOMA) that incorporates a surrogate model into the local search. In the framework of interval multi-objective memetic algorithms (IMOMAs), the fitness function of a local search is first defined by both the contribution of an individual to hyper-volume and the imprecision of the individual, and then a support vector machine (SVM) is trained and employed to evaluate local individuals so as to cut down the high computational cost of IMOMAs and further reduce the imprecision of the final front. The proposed algorithm was tested on 10 benchmark IMOPs and an IMOP in solar desalination. The empirical results indicate that SA-IMOMA is more economical than non-surrogate IMOMAs and superior to non-local-search IP-MOEA.

关键词： multi-objective optimization problem interval memetic algorithm Surrogate model

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multi-objective Energy optimization Of Locomotions For Underwater Snake Robot

Multi-objective Energy Optimization Of Locomotions For Under...

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作者： Kenny Duy Luong Norwegian University of Science and Technology

学位级别：硕士

Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. Tis thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs). A well-known issue of the USRs, or any AUVs, is the long- term autonomy. To achieve this, energy efcient approaches are required. Many studies have considered single-objective optimization problems regarding the energy efciency of the USR, but almost none with multi-objective optimization problems (MOPs). Tis thesis presents MOPs of diferent locomotions of the USR. Te presented MOPs consider the energy efcient optimiza- tion of maximizing the forward velocity, while minimizing the power consumption of the USR. For computing the efcient motion paterns, two multi-objective Evolutionary Algorithms (MOEAs) called Non-dominated Sort Genetic Algorithm II (NSGA-II), and Hypervolume Estimation Algo- rithm for multi-objective optimization (HypE) are applied. A challenging topic of the USR, is their adaptability of diferent locomotions. Diferent locomotions of the USR give rise to diferent search spaces for optimization. We present simulation studies of the two most common snake locomotions: (i) lateral undulation and (ii) eel-like motion. Furthermore, we also present and in- vestigate three altered motion patern of the USR. Te aim of the altered locomotions is to let the MOEAs generate efcient locomotions through evolutionary, which we do not know the gait of. From the simulation results, it turns out that one of the altered motion patern approximates a motion similar to the lateral undulation. Tis motion patern is generated based on Fourier se- ries. Te obtained simulation results are based on optimization with optimal Genetic Algorithm (GA) parameters, found by numerous presimulations of the MOPs. Since this is multi-objective optimization problems, the end results will be in the form of Pareto fronts. Tese Pareto fronts can be used as trade-ofs for selecting th

关键词： Underwater snake robot Energy efciency multi-objective Evolutionary Algorithm Non-dominated Sort Genetic Algorithm II Hypervolume Estimation Algorithm for multi-objective optimization Principal Component Analysis Partial Least Square Regression multi-objective optimization problem Pareto fronts Patern generators

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An Improved multi-objective optimization for Workflow Scheduling in Cloud Platform

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JOURNAL OF INTERNET TECHNOLOGY 2017年第3期18卷 589-599页

作者： Prathibha, Soma Latha, B. Sumathi, G. Sri Sai Ram Engn Coll Dept Informat Technol Madras Tamil Nadu India Anna Univ Madras Tamil Nadu India Sri Sai Ram Engn Coll Dept Comp Sci & Engn Madras Tamil Nadu India Sri Venkateswara Coll Engn Dept Informat Technol Madras Tamil Nadu India

Cloud computing is a promising research domain used to enhance the performance of information systems and applications used at our daily life. Workflow scheduling in cloud platform is a complex optimization problem because of its heterogeneous nature, complex billing models and infinite resources requirements which has lead the users to look for an optimal or a suboptimal solution that satisfy multiple competing goals such as minimum makespan and energy efficient solutions. The focus of this paper is to schedule workflow applications, optimizing makespan and energy consumption. A high-performance method for scheduling workflow on cloud computing platform will be presented in this paper, called non-dominated sorting particle swarm optimization (NSPSO). The proposed method is evaluated on synthetic representation of real world scientific workflow applications. The simulation results shows that the proposed method can provide better workflow scheduling solution for cloud when compared with other state-of-the-art methods.

关键词： Cloud computing Workflow scheduling multi-objective optimization problem

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An Improved multi-objective optimization for Workflow Scheduling in Cloud Platform

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网际网路技术学刊 2017年第3期18卷 589-599页

作者： Soma Prathibha B. Latha G. Sumathi

关键词： Cloud computing Workflow scheduling multi-objective optimization problem

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Interactive Reference Region Based multi-objective Evolutionary Algorithm Through Decomposition

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IEEE ACCESS 2016年第99期4卷 7331页

作者： Liu, Ruochen Wang, Ruinan Feng, Wen Huang, Junjun Jiao, Licheng Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

Many evolutionary multi-objective optimization (EMOs) methodologies have been proposed and shown a great potential in approximating the entire Pareto front. While in real world, what decision makers (DMs) want is one or several solutions to satisfy their requirements. It has become a hot problem that dynamically using preference information provided by DMs during the optimization process guides the search of EMO algorithms. An interactive reference region-based evolutionary algorithm through decomposition is proposed, denoted as RR-MOEA/D in this paper, which focuses the search on the desire of DMs to save computational resources. MOEA/D, as a well-known multi-objective optimization algorithm, is used as a basic framework here. In MOEA/D, by dealing with the sub-problems in the preference region and ignoring uninterested ones, the solutions obtained can converge to the regions which the DM prefers on the Pareto front and the computational complexity can be saved to a great extent. At each interaction, a humanized and simple interactive condition is adopted so that the reference region can be changed in a very intuitive way if the DM is unsatisfied the results in the interactive process. A rapid interaction is designed and a set of rough solutions can be obtained quickly whenever the preference information is changed. The proposed algorithm is tested on several benchmark problems and the experimental results show that the proposed algorithm can take full use of preference information and successfully converge to the reference region due to its reasonable and simple interaction mechanism.

关键词： Interactive preference multi-objective optimization problem reference region selection mechanism

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