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A multi-objective Design Approach for the c Chart Considering Taguchi Loss Function

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2014年第8期30卷 1179-1190页

作者： Lupo, Toni Univ Palermo Dipartimento Ingn Chim I-90128 Palermo Italy

The present paper proposes a multi-objective design approach for the c chart, considering in the optimization process of the chart parameters both the statistical and the economic objectives. In particular, the minimization of the hourly total quality related costs is the considered objective to carry out the economic goal, whereas the statistical objective is reached by the minimization the out-of-control average run length of the chart. A mixed integer non-linear constrained mathematical model is formulated to solve the treated multi-objective optimization problem, whereas the Pareto optimal frontier is described by the epsilon-constraint method. In order to show the employment of the proposed approach, an illustrative example is developed and the related considerations are given. Finally, some sensitivity analysis is also performed to investigate the effects of operative and costs parameters on the chart performance. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： statistical process control Taguchi loss function multi-objective optimization problem epsilon-constraint method non-linear constrained mathematical programming Model Pareto optimal frontier

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Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

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SOFT COMPUTING 2012年第6期16卷 989-1008页

作者： Nandi, Arup Kumar Datta, Shubhabrata Deb, Kalyanmoy Cent Mech Engn Res Inst CSIR CMERI Durgapur 713209 W Bengal India Birla Inst Technol Jasidih 814142 Deoghar India Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.

关键词： multi-objective optimization problem Evolutionary algorithm Particle-reinforced polyurethane mould material Soft tooling process Cooling time

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multi-objective probabilistic reactive power and voltage control with wind site correlations

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ENERGY 2014年 66卷 810-822页

作者： Zare, Mohsen Niknam, Taher Azizipanah-Abarghooee, Rasoul Amiri, Babak Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Univ Sydney Fac Engn & IT Sydney NSW 2006 Australia

This paper proposes a multi-objective probabilistic reactive power and voltage control in distribution networks using wind turbines, hydro turbines, fuel cells, static compensators and load tap changing transforms. The objective functions are total electrical energy costs, the electrical energy losses, total emissions produced, and voltage deviations during the next day. Since the wind sources and load demand have intermittent characteristics, a probabilistic load flow based on 2m + 1 point estimated method is used to investigate the objective functions. The correlation in wind speed is considered as the distances between WTs are not large in distribution systems. Furthermore, a multi-objective modified bee swarm optimization is proposed to solve the optimization problem by defining a set of non-dominated points as the solutions. A fuzzy based clustering is used to control the size of the repository and a niching method is utilized to choose the best solution during the optimization process. Performance of the proposed algorithm is tested on a 69-bus distribution feeder. The results confirm the necessity of modeling the reactive power and voltage control problem in a stochastic framework. Also, the effects of wind site correlations on different objective functions are discussed completely. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Modified bee swarm optimization multi-objective optimization problem Point estimated methods Reactive power and voltage control Wind site correlation

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Improved RM-MEDA with local learning

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SOFT COMPUTING 2014年第7期18卷 1383-1397页

作者： Li, Yangyang Xu, Xia Li, Peidao Jiao, Licheng Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

In this paper, local learning is proposed to improve the speed and the accuracy of convergence performance of regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), a typical multi-objective optimization algorithm via estimation of distribution. RM-MEDA employs a model-based method to generate new solutions, however, this method is easy to generate poor solutions when the population has no obvious regularity. To overcome this drawback, our proposed method add a new solution generation strategy, local learning, to the original RM-MEDA. Local learning produces solutions by sampling some solutions from the neighborhood of elitist solutions in the parent population. As it is easy to search some promising solutions in the neighborhood of an elitist solution, local learning can get some useful solutions which help the population attain a fast and accurate convergence. The experimental results on a set of test instances with variable linkages show that the implement of local learning can accelerate convergence speed and add a more accurate convergence to the Pareto optimal.

关键词： multi-objective optimization problem Estimation of distribution algorithm Local learning

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multi-objective operation management of a multi-carrier energy system

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ENERGY 2015年 88卷 430-442页

作者： Shabanpour-Haghighi, Amin Seifi, Ali Reza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

In this paper, a multi-objective optimization approach for multi-carrier energy networks is discussed. A multi-carrier energy network is a system consists of various types of energy carrier such as electricity, natural gas, and heat. Minimizing the total cost of operation of such a system is a typical objective for optimization while another important objective is to minimize the total emission generated by the whole network. It is shown in the paper that the cost and emission functions are two opposite objectives that decreasing one of them would increase the other one and vice versa. Therefore, a multi-objective optimization should be utilized to obtain the global optima of the problem based on the priority of each objective. According to the large size of the problem in actual networks, this could be a non-linear, non-convex, non-smooth, and high-dimension optimization problem that mathematical techniques could be trapped in local minima. Hence, it is better to use evolutionary techniques instead. To do so, a fuzzy decision making method is proposed in this paper which is merged with the well-known modified teaching-learning based optimization algorithm. This approach is implemented and applied to a typical multi-carrier energy network to verify the proposed methodology. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization problem multi-carrier energy system Energy hub Heuristic algorithm

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Maximizing network lifetime based on transmission range adjustment in wireless sensor networks

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COMPUTER COMMUNICATIONS 2009年第11期32卷 1316-1325页

作者： Song, Chao Liu, Ming Cao, Jiannong Zheng, Yuan Gong, Haigang Chen, Guihai Univ Elect Sci & Technol China Sch Engn & Comp Sci Chengdu 610054 Peoples R China Hong Kong Polytech Univ Dept Comp Internet & Mobile Comp Lab Hong Kong Hong Kong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Peoples R China

In a wireless sensor network (WSN), the unbalanced distribution of communication loads often causes the problem of energy hole, which means the energy of the nodes in the hole region will be exhausted sooner than the nodes in other regions. This is a key factor which affects the lifetime of the networks. In this paper we propose an improved corona model with levels for analyzing sensors with adjustable transmission ranges in a WSN with circular multi-hop deployment (modeled as concentric coronas). Based on the model we consider that the right transmission ranges of sensors in each corona is the decision factor for optimizing the network lifetime after nodes deployment. We prove that searching optimal transmission ranges of sensors among all coronas is a multi-objective optimization problem (MOP). which is NP hard. Therefore, we propose a centralized algorithm and a distributed algorithm for assigning the transmission ranges of sensors in each corona for different node distributions. The two algorithms can not only reduce the searching complexity but also obtain results approximated to the optimal solution. Furthermore, the simulation results of our solutions indicate that the network lifetime approximates to that ensured by the optimal under both uniform and non-uniform node distribution. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Energy hole problem multi-objective optimization problem NP hard

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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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A Hyper Heuristic Algorithm for Efficient Resource Allocation in 5G Mobile Edge Clouds

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第1期23卷 29-41页

作者： Laboni, Nadia Motalib Safa, Sadia Jahangir Sharmin, Selina Razzaque, Md. Abdur Rahman, Md. Mustafizur Hassan, Mohammad Mehedi Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka 1000 Bangladesh Jagannath Univ Dept Comp Sci & Engn Dhaka 1100 Bangladesh King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Res Chair Pervas & Mobile Comp Riyadh 11543 Saudi Arabia

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of Ant Colony, Whale, Sine-Cosine, and Henry Gas Solubility optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

关键词： Servers Resource management optimization 5G mobile communication Delays Task analysis Mobile computing 5G Mobile edge cloud resource allocation load balancing multi-objective optimization problem hyper-heuristic algorithm

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multi-objective energy management approach in distribution grid integrated with energy storage units considering the demand response program

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第13期44卷 10662-10681页

作者： Lotfi, Hossein Islamic Azad Univ Dept Elect Engn Neyshabur Branch Neyshabur Iran

The penetration of renewable energy sources and energy storage (ES) units into the distribution system has increased, and it is important to examine their effect on the systems' operation scheme and security. Voltage stability index is defined as a security objective function, and its improvement by energy management in the distribution network is a major challenge in this study. The dynamic distribution feeder reconfiguration (DDFR) is introduced as an efficient approach for energy management in the distribution network, considering energy loss, voltage stability index, and operational cost as objective functions in the presence of distributed generators, solar PV panels, ES units, and capacitors. The demand response program, including interruptible/curtailable service, is proposed to enable energy consumers to rethink their energy consumption patterns based on incentive and punitive policies. A modified particle swarm optimization algorithm is presented to solve the considered optimization problem. The suggested approach is tested on the 95-node test system and its superiorities are shown through comparison with other evolutionary algorithms. Based on the obtained results, after presenting a new model based on DDFR for energy management in the distribution system, energy loss and voltage stability are reduced by 25% and 2.8%, respectively. After applying the demand response program in the proposed model, energy loss and operational cost are reduced by 26% and 5.9%, respectively.

关键词： distribution feeder reconfiguration energy management modified particle swarm optimization multi-objective optimization problem renewable energy resources voltage stability index

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Game Model-based Co-evolutionary Algorithm with Non-dominated Memory and Euclidean Distance Selection Mechanisms for multi-objective optimization

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2011年第5期9卷 924-932页

作者： Park, Seung-Min Ko, Kwang-Eun Park, Junheong Sim, Kwee-Bo Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

Many real-world problems involve simultaneous optimization of several incommensurable and often competing objectives. In the search for solutions to multi-objective optimization problems (MOPs), we find that there is no single optimum but rather a set of optimums known as the "Pareto optimal set". Co-evolutionary algorithms are well suited to optimization problems which involve several often competing objectives. Co-evolutionary algorithms are aimed at evolving individuals through individuals competing in an objective space. In order to approximate the ideal Pareto optimal set, the search capability of diverse individuals in an objective space can be used to determine the performance of evolutionary algorithms. Non-dominated memory and Euclidean distance selection mechanisms for co-evolutionary algorithms have the goal of overcoming the limited search capability of diverse individuals in the population space. In this paper, we propose a method for maintaining population diversity in game model-based co-evolutionary algorithms, and we evaluate the effectiveness of our approach by comparing it with other methods through rigorous experiments on several MOPs.

关键词： Co-evolutionary evolutionary algorithm multi-objective optimization problem pareto optimal set

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