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IEEE Symposium on Computers and Communications (ISCC)

作者： Liang, Shuang Fang, Zhiyi Sun, Geng Li, Jiahui Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781665497923

Unmanned aerial vehicles (UAVs) Communications and networks are of paramount importance in the 5G/6G networks. However, how to deploy the limited on-board energy and restricted transmit power of the UAV so that enhancing the communication efficiency is still a key issue. In this work, we consider a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). For achieving a better energy efficiency, we formulate a hovering and motion energy consumption multi-objective optimization problem (HMECMOP) of UAV-enable CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs and the order of communicating with different BSs. Then, we propose an improved multi-objective multi-verse optimizer (IMOMVO) to solve the formulated HMECMOP. IMOMVO uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to deal with the complex solution space which contains continuous and discrete solutions, so that making the algorithm more suitable for solving the formulated optimization problem. Simulation results demonstrate that the proposed algorithm is effective for solving the HMECMOP and it has better performance than some other comparison algorithms.

关键词： UAV communications collaborative beamforming energy efficiency multi-objective optimization problem swarm intelligence

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Landscape Analysis of multi-objective Control of Fluidized Beds

Landscape Analysis of Multi-objective Control of Fluidized B...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Jamil, Iffat Mostaghim, Sanaz van Wachem, Berend Cheron, Victor Hausmann, Max Otto von Geuricke Univ Magdeburg Germany

ISBN: (纸本)9798400701207

Fluidized beds are widely used in various industrial applications that require efficient mixing, heat and mass transfer between gas and solid particles. This paper explores the relationship between input parameters of inlet gas velocity into the fluidized bed, by varying the amplitude and the frequency of the velocity, and the objective outputs, such as the total number of bubbles per unit time and the average bubble size in a fluidized bed, using CFD-DEM simulations. The goal of the paper is to define a multi-objective optimization problem and provide an insight into the landscape in the search space. Since the CFD-DEM simulation of a fluidized bed requires a significant computational effort, this landscape analysis is aimed to give an overview and a ground truth for further research in this area.

关键词： CFD-DEM simulation fluidizied bed multi-objective optimization problem

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multi-objective interval fractional programming problems : An approach for obtaining efficient solutions

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OPSEARCH 2015年第1期52卷 156-167页

作者： Bhurjee, Ajay Panda, Geetanjali Indian Inst Technol Kharagpur Kharagpur 721302 WB India Indian Inst Technol Kharagpur Fac Math Dept Math Kharagpur 721302 WB India

This paper addresses a general multi-objective fractional programming problem whose parameters in the objective functions and constraints are intervals. Existence of the efficient solution of this model is studied. A methodology is developed to determine its efficient solutions. This methodology is illustrated through a numerical example.

关键词： Interval optimization multi-objective optimization problem Efficient solution Fractional programming problem

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Nonlinear Equation Systems Solved by many-objective Hype

Nonlinear Equation Systems Solved by many-objective Hype

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IEEE Congress on Evolutionary Computation (CEC)

作者： Qin, Sha Zeng, Sanyou Dong, Wei Li, Xi China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Shijiazhuang Univ Econ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China

ISBN: (纸本)9781479974924

A difficulty in solving nonlinear equation systems (NESs) stays in finding all the solutions for NES. This paper uses multi-objective evolutionary techniques to overcome it. We converted the NES into a multi-objective optimization problem (MOP) with a parameter C. The Pareto-optimal set of the MOP becomes the solutions of the NES when the parameter C gets to infinity. Next, a multi-objective evolutionary algorithm (MOEA) is used to solve the transformed MOP, during which C is gradually approaching infinity. A significant feature of this algorithm is that there is one-to-one relationship between the Pareto optimal set and the Pareto front, which suggests that different solutions have different objective values in the MOP. Thus the MOEA can find multi-solutions of the NES in a single run. Since the MOP is a multi-objective problem in many cases, this paper applies an advanced multi-objective evolutionary algorithm (i.e., Hype algorithm) to solve NES. Our experiment shows better results than or competitive to the four mentioned single-objective optimization in a set of test cases.

关键词： multi-objective optimization problem transformational MOP Nonlinear equation systems Hype algorithm

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Parameter Selection for Particle Swarm optimization Based on Stochastic multi-objective optimization

Parameter Selection for Particle Swarm Optimization Based on...

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Chinese Automation Congress (CAC)

作者： Xu, Ming Gu, JiangPing Zhejiang Univ Technol Coll Mech Engn Hangzhou Zhejiang Peoples R China Zhejiang Univ Technol Coll Educ Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781467371896

Parameter selection in particle swarm optimization algorithm had great influence on its performance. This study presented a method of parameter optimization for the particle swarm optimization algorithm based on Stochastic multi-objective optimization. Based on the analysis of the relationship of inertia weight, cognitive coefficient and social coefficient, a stochastic convergence speed index function and a stochastic convergence precision index function for the standard particle swarm optimization algorithm model were analyzed and established, where some of the parameters were stochastic. Then, as two targets of these index functions in a multi-objective optimization problem, a kind of multi-objective optimization method was applied to optimize the parameters. By optimizing the two index functions, a Pareto set and corresponding parameter selection guidelines were derived to guarantee that the particle swarm optimization algorithm had a good convergent speed and accuracy in the stochastic sense. In practical application, it provided some theoretical basis, and had a helpful guiding significance.

关键词： particle swarm optimization parameter selection multi-objective optimization multi-objective optimization problem

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Optimal Placement of Data Concentrators and Repeaters in PLC-enabled Smart Grids

Optimal Placement of Data Concentrators and Repeaters in PLC...

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14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Stiri, Souhaima Chaoub, Abdelaali Grilo, Antonio Bennani, Rachid Lakssir, Brahim Tamtaoui, Ahmed INPT MUSICS Team STRS Lab Rabat Morocco MAScIR D&MSD Lab Rabat Morocco Univ Lisbon IST Lisbon Portugal INESC ID Lisbon Portugal

ISBN: (纸本)9781665435970

The focus of this paper is on Power Line Communication (PLC) technology which has a great potential to accommodate the huge amount of data produced by different applications and nodes in the context of Smart Grids (SGs). In particular, this paper dives deeper into the planning and placement of Data Concentrator (DC) in various territorial categories: urban, suburban and rural environments. Supplemental repeaters, usually omitted in previous stat-of-the-art, are inserted throughout the electrical grid to reach the entire network. A multi-criteria optimization approach has been followed and the popular NSGA-II algorithm has been exploited to find a good trade-off between the conflicting performance and profitability objectives. Results of extensive numerical simulation are provided confirming the effective need of repeaters especially in remote areas where Smart Meters (SMs) are sparsely distributed. Moreover and despite the destructive influence of fading and noise along with the involved investments, PLC is still able to provide a satisfactory balance between performance and cost.

关键词： Smart Metering PLC Data Concentrator and Repeater Placement multi-objective optimization problem multi-objective Evolutionary Algorithm

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Weighted Tradeoff between Effective Capacity and Energy Efficiency

Weighted Tradeoff between Effective Capacity and Energy Effi...

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IEEE International Conference on Communications (ICC)

作者： Yu, Wenjuan Musavian, Leila Ni, Qiang Univ Lancaster Sch Comp & Commun InfoLab21 Lancaster England

ISBN: (纸本)9781467364324

This paper proposes a new power allocation technique to jointly optimize link-layer energy efficiency (EE) and effective capacity (EC) of a Rayleigh flat-fading channel with delay-outage probability constraints. Specifically, EE is formulated as the ratio of EC to the sum of transmission power and rate-independent circuit power consumption. A multi-objective optimization problem (MOP) to jointly maximize EE and EC is then formulated. By introducing importance weight into the MOP, we can flexibly change the priority level of EE and EC, and convert the MOP into a single-objective optimization problem (SOP) which can be solved using fractional programming. At first, for a given importance weight and a target delay-outage probability, the optimum average transmission power level to maximize the SOP is found. Then, the optimal power allocation strategy is derived based on the obtained average input power level. Simulation results confirm the analytical derivations and further show the effects of circuit power, importance weight, and transmission power constraint limit on the achievable tradeoff performance.

关键词： multi-objective optimization problem Quality-of-service Delay-outage probability constraint Effective capacity Energy efficiency Fractional programming

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MPSO-Based Model of Train Operation Adjustment

MPSO-Based Model of Train Operation Adjustment

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6th International Conference on Green Intelligent Transportation System and Safety (GITSS)

作者： Zhang, Lanxia Qin, Yong Meng, Xuelei Wang, Li Zhu, Tao Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China Beijing Jiaotong Univ Beijing Res Ctr Urban Traff Informat Sensing & Se Beijing 100044 Peoples R China Informat Technol Chinese Railway Beijing 100844 Peoples R China

Scheduling trains in a railway network is a fundamental operational problem in the railway industry. This paper sets up multi objective optimal model of train operation adjustment, whose optimization objective is to reduce the train delay time and the numbers of delay train. Since the model is established as an NP complete problem, a multi-objective particle swarm optimization algorithm (MPSO) is proposed to solve the complex problem. Considering the strategy of dispatcher' preference, MPSO can get a set of Pareto solutions in the actual train operation adjustment problems. The actual experiment, taking Beijing-Shanghai highspeed railway as example, is conducted to validate the feasibility of the algorithm compared with the basic particle swarm optimization algorithm (PSO). Results demonstrate that the model can capture the characteristics of the practical dispatching problem. MPSO is efficient for train operation adjustment and provides better solutions than the traditional approaches. (C) 2016 Published by Elsevier Ltd.

关键词： Train operation adjustment multi-objective optimization problem MPSO

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An Improved Competitive Mechanism based Particle Swarm optimization Algorithm for multi-objective optimization 10

An Improved Competitive Mechanism based Particle Swarm Optim...

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10th International Conference on Information Science and Technology (ICIST)

作者： Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai Open Univ Hong Kong Sch Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781728155586

In this paper, an improved Competitive Mechanism-based Particle Swarm optimization algorithm called MCMOPSO is presented for multi -objective optimization. The algorithm consists of two main contributions: a new leader selection and the analysis of inertia weight. The new multi competition leader selection is introduced which is based on the pairwise competition. It will not only guide the particles to fly to the winner by comparing the nearest angle for two randomly selected elite particles, but also lead the particles to fly to the winner by comparing the nearest angle or farthest angle for several randomly selected elite particles in each iteration. To strike a balance between the exploration and exploitation of the velocity update equation for the original competitive mechanism-based MOPSOalgorithm (CMOPSO), the influence of various inertia weights is investigated to control the previous velocity of each particle. The simulation results show that the proposed algorithm is outperformed four other famous multi -objective particle swarm optimization algorithms in thirty-seven benchmark test problems in terms of inverted generational distance.

关键词： competitive mechanism multi-objective optimization problem particle swarm optimization

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Managing Genetic Algorithm Parameters to Improve SegGen, a Thematic Segmentation Algorithm

Managing Genetic Algorithm Parameters to Improve SegGen, a T...

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24th International Workshop on Database and Expert Systems Applications (DEXA)

作者： Saygili, Neslihan Sirin Acarman, Tankut Amghar, Tassadit Levrat, Bernard Istanbul Univ Inst Sci Galatasaray Istanbul Turkey Univ Angers LERIA Angers France

ISBN: (纸本)9780769550701

SegGen [1] is a linear thematic segmentation algorithm grounded on a variant of the Strength Pareto Evolutionary Algorithm [2] and aims at optimizing the two criteria of the Salton's [3] definition of segments: a segment is a part of text whose internal cohesion and dissimilarity with its adjacent segments are maximal. This paper describes improvements that have been implemented in the approach taken by SegGen by tuning the genetic algorithm parameters according with the evolution of the quality of the generated populations. Two kinds of reasons originate the tuning of the parameters and have been implemented here. First as it could be measured by the values of global criteria of the population quality, the global quality of the generated populations increases as the process goes and it seems reasonable to set values to parameters and define new operators, which favor intensification and diminish diversification factors in the search process. Second since individuals in the populations are plausible segmentations it seems reasonable to weight sentences in the current segmentation depending on their distance to the boundaries of the segment they belong to for the calculus of similarities between sentences implied in the two criteria to be optimized. Although this tuning of the parameters of the algorithm currently rests on estimations based on experiments, first results are promising.

关键词： thematic segmentation genetic algorithm multi-objective optimization problem

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