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A survivable design of last mile communication networks using multi-objective genetic algorithms

MEMETIC COMPUTING 2016年第2期8卷 97-108页

作者： Bui, Lam Thu Binh, Huynh Thi Thanh Le Quy Don Tech Univ Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam

In this paper, we are interested in the survivable network design problem (SNDP) for last mile communication networks called (L-SNDP). Given a connected, weighted, undirected graph G = (V, E);a set of infrastructure nodes and a set of customers C including two customer types where customers in the subset C1 require a single connection (type-1) and customers in the subset C2 need to be redundantly connected (type-2). The aim is to seek a subgraph of G with the smallest weight in which all customers are connected to infrastructure nodes and the connections are protected against failures. This is a NP-hard problem and it has been solved only with the objective of minimizing the network cost. In this paper, we introduce a new multi-objective approach to solve L-SNDP called ML-SNDP. These objectives are to minimize the network cost (total cost) and to minimize the maximal amount of sharing links between connections. Results of computational experiments reported show the efficiency of our proposal.

关键词： Survivable network design multi-objective genetic algorithm

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Surrogate-Assisted multi-objective genetic algorithms for Fuzzy Rule-Based Classification

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第6期20卷 1938-1955页

作者： Kalia, Harihar Dehuri, Satchidananda Ghosh, Ashish Cho, Sung-Bae Seemanta Engn Coll Dept Comp Sci Mayurbhanj 757086 Odisha India Fakir Mohan Univ Dept Informat & Commun Technol Balasore 756019 Odisha India Indian Stat Inst Machine Intelligence Unit 203 BT Rd Kolkata 700108 India Indian Stat Inst Ctr Soft Comp Res 203 BT Rd Kolkata 700108 India Yonsei Univ Dept Comp Sci Soft Comp Lab 134 Shinchon Dong Seoul 120749 South Korea

In this paper, we present a surrogate-assisted multi-objective genetic algorithm to mine a small number of linguistically interpretable fuzzy rules for high-dimensional classification task in the realm of data mining. However, the difficulties like (1) handling of high-dimensional problems by fuzzy rule-based systems (i.e., the exponential increase in the number of fuzzy rules with the number of input variables), (2) the deterioration in the comprehensibility of fuzzy rules when they involve many antecedent conditions, and (3) the optimization of multiple objectives in fuzzy rule-based system may stand as pertinent issues. Hence, to combat with the aforesaid issues, we design the problem as a combinatorial optimization problem with three objectives: to maximize the number of correctly classified training patterns, minimize the number of fuzzy rules, and minimize the total number of antecedent conditions. We optimize these objectives of the fuzzy rule-based system by using a multi-objective genetic algorithm. Further to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multi-objective genetic algorithm for fuzzy rule-based system, a radial basis neural network surrogate model is adapted. This approach searches for non-dominated rule sets with respect to these three objectives. The performance of the surrogate-assisted model is evaluated through a few benchmark datasets obtained from knowledge extraction based on evolutionary learning data repository. The experimental outcome confirm that this model is competitive compared to the non-surrogate-assisted model. However, the performance of the model has drawn a clear edge over rule mining approaches like Decision Table, JRip, OneR, PART, and ZeroR.

关键词： Classification rule Fuzzy set genetic algorithm multi-objective genetic algorithm Surrogate-assisted model

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Optimal corrective actions for power systems using multi-objective genetic algorithms

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ELECTRIC POWER SYSTEMS RESEARCH 2009年第5期79卷 722-733页

作者： El Ela, Adel A. Abou Spea, Shaimaa R. Minofiya Univ Fac Engn Dept Elect Engn Minofiya Egypt

In this paper, optimal corrective control actions are presented to restore the secure operation of power system for different operating conditions. genetic algorithm (GA) is one of the modern optimization techniques, which has been successfully applied in various areas in power systems. Most of the corrective control actions involve simultaneous optimization of several objective functions, which are competing and conflicting each other. The multi-objective genetic algorithm (MOGA) is used to optimize the corrective control actions. Three different procedures based on GA and MCCA are proposed to alleviate the violations of the overloaded lines and minimize the transmission line losses for different operation conditions. The first procedure is based on corrective switching of the transmission lines and generation re-dispatch. The second procedure is carried out to determine the optimal siting and sizing of distributed generation (DG). the third procedure is concerned into solving the generation-load imbalance problem using load While, shedding. Numerical simulations are carried out on two test systems in order to examine the validity of the proposed procedures. (C) 2008 Elsevier B.V. All rights reserved

关键词： multi-objective genetic algorithm Corrective control actions Transmission lines switching Generation re-dispatch Distributed generation Load shedding

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Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

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APPLIED SOFT COMPUTING 2017年 52卷 863-876页

作者： Pierre, Djamalladine Mahamat Zakaria, Nordin Univ Teknol PETRONAS High Performance Comp Ctr Tronoh 32610 Perak Malaysia

This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator. (C) 2016 Elsevier B.V. All rights reserved

关键词： multi-objective vehicle routing problems multi-objective genetic algorithm Crossover

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Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2009年第5期37卷 447-461页

作者： Li, G. Li, M. Azarm, S. Al Hashimi, S. Al Ameri, T. Al Qasas, N. Univ Maryland Dept Mech Engn College Pk MD 20742 USA Petr Inst Dept Chem Engn Abu Dhabi U Arab Emirates

Applications of multi-objective genetic algorithms (MOGAs) in engineering optimization problems often require numerous function calls. One way to reduce the number of function calls is to use an approximation in lieu of function calls. An approximation involves two steps: design of experiments (DOE) and metamodeling. This paper presents a new approach where both DOE and metamodeling are integrated with a MOGA. In particular, the DOE method reduces the number of generations in a MOGA, while the metamodeling reduces the number of function calls in each generation. In the present approach, the DOE locates a subset of design points that is estimated to better sample the design space, while the metamodeling assists in estimating the fitness of design points. Several numerical and engineering examples are used to demonstrate the applicability of this new approach. The results from these examples show that the proposed improved approach requires significantly fewer function calls and obtains similar solutions compared to a conventional MOGA and a recently developed metamodeling-assisted MOGA.

关键词： Metamodeling multi-objective genetic algorithm Adaptive DOE Kriging

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Subpopulation preference adjective non-dominated sorting genetic algorithm for multi-objective capacity expansion for matured fabs

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Kuo, Hsuan-An Peng, Chia-Ching Chien, Chen-Fu Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Natl Tsing Hua Univ AIMS Fellows Execut Master Program Hsinchu 30013 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management 101 Sect 2 Kuang Fu Rd Hsinchu 30013 Taiwan

Capacity expansion strategy consists of fulfilling the demand with capacity portfolio via alternative configurations with long lead time for procurement and installation. Sequential dependent decisions including demand planning and cost structure of capital expenditure shall be taken into consideration for achieving enterprise profitability. Though a number of studies have been done to address related issues, limitations of existing approaches can be traced in part to the lack of a systematic framework in which multiple objectives of related total resource management problems can be considered and integrated. Little research has been done to address the present problem for capacity expansion for matured fabs from the perspective of total resource management. To fill the gaps, this study applies the concept of total resource management to integrate operational strategies and the overall usage of resources. This study develops a capacity expansion model with multiple objectives including minimizing resource costs, maximizing overall return, maximizing revenue, and minimizing capacity risk. Since the formulation of the multi-objectives can be non-linear and the size of the problem is increasing, it is difficult to solve the problem in reasonable time for practical use. A subpopulation preference adjective non-dominated sorting genetic algorithm (SPANS-GA) is developed to solve the decision problem. An empirical study in a leading semiconductor company in Taiwan is conducted for validation. The results have shown practical viability of the developed solution for multi-objective capacity planning for matured wafer fabs for total resource management.

关键词： Capacity expansion Total resource management multi-objective genetic algorithm Matured fabs Semiconductor manufacturing

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multi-objective Optimization of a Solar Chimney Power Plant with Inclined Collector Roof Using genetic algorithm

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ENERGIES 2016年第11期9卷 971页

作者： Gholamalizadeh, Ehsan Kim, Man-Hoe Kyungpook Natl Univ Sch Mech Engn Daegu 41566 South Korea

This paper presents an optimization of a solar chimney power plant with an inclined collector roof using genetic algorithms. Five design parameters that affect the system performance are the collector radius, collector inlet height, collector outlet height, chimney height and diameter. A multi-objective design to simultaneously optimize three conflicting objectives including system efficiency, power output and expenditure is used. Based on this approach, obtaining the best combination of the possible geometrical parameters, performance of two built pilot power plants in Kerman (Iran) and Manzanares (Spain) are optimized thermo-economically. The heights of the zero-slope collectors of the Kerman and Manzanares systems are 2 m and 1.85 m, respectively. The results show that in the Kerman pilot the optimal collector inlet and outlet heights are 1.5 m and 2.95 m, respectively, while those optimal heights in the Manzanares prototype are 1.5 m and 4.6 m, respectively. It is found that selecting the optimal collector roof configuration in addition to the other design parameters has a significant effect in the system optimization process.

关键词： solar chimney power plant inclined collector roof renewable energy multi-objective genetic algorithm

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multi-objective Optimization of RTAB-Map parameters using genetic algorithm for indoor 2D SLAM

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Procedia Computer Science 2024年 250卷 172-181页

作者： Nagamalar Nagarajan Hanxiang Zhang Wei Liu Jason Gu Electrical and Computer Engineering Dalhousie University 1459 Oxford Street Halifax B3H 4R2 Canada Mechanical and Energy Engineering Southern University of Science and Technology 1088 Xueyuan Avenue Shenzhen 518055 China

Currently, Robot Operating System (ROS) provides multiple packages to implement different Simultaneous Localization and Mapping (SLAM) approaches. To effectively obtain sensor data, these packages use parameters whose values are set from prior knowledge and experience with robots and SLAM. In this paper, using a multi-objective genetic algorithm (MOGA) to optimize the values for these parameters is proposed. MOGA allows trade-offs between the objectives using Pareto dominance techniques. Three parameters from the RTAB-Map package are considered for optimization using three different MOGA mechanisms, Dominance Count, Dominance Rank and Switching Fitness. The quality of the map generated for every set of parameters is taken as the indicator of its performance. The number of corners, number of contours and the proportion of occupied cells in the map are used as quantitative measures of map quality. Finally, results obtained from testing the algorithm in simulation are used to test a Quanser QBot2 robot.

关键词： Simultaneous localization and mapping RTAB-Map robot operating system multi-objective genetic algorithm dominance count dominance rank switching fitness

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Space Active Noise Control System Design with multi-objective genetic algorithms

Space Active Noise Control System Design with Multi-objectiv...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Liu Huideng Qiu ARui Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781424478354

The model for the space active noise control system is investigated in this paper and is converted to a multi-objective optimization problem with constraints, of which the positions of secondary speakers and error sensors are the decision variables, the summation of the squared pressure at all points within the noise quiet zone and the total source strength for the secondary speakers are the multi-objective functions. The multi-objective genetic algorithms and simple genetic algorithm are implemented to solve the optimization problem so as to determine the appropriate positions of the secondary speakers and error sensors. The large sound pressure reductions within the noise quiet zone to control the single tone primary noise and motor operating noise show that the optimal schemes obtained by the multi-objective genetic algorithms are efficient.

关键词： Active noise control(ANC) space multi-objective genetic algorithm

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multi-objective genetic algorithms for Trajectory Optimization of Space Manipulator

Multi-Objective Genetic Algorithms for Trajectory Optimizati...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhengxiong Liu Panfeng Huang Jie Yan Gang Liu Research Center for Intelligent Robotics College of Astronautics Northwestern Polytechnical University Infineon Technologies(Xi'an)Ltd

ISBN: (纸本)9781424427994

This paper propose a multi-objective optimization algorithm to optimize the motion path of space manipulator with multi-objective function. In this formulation, multi-objective genetic algorithm (MOGA) is used to minimize the multi-objective function. The planning procedure is performed in joint space and with respect to all constraints, such as joint angle constraints, joint velocity constraints, torque constraints. We use a MOGA to search the optimal joint inter-knot parameters in order to realize the optimal motion trajectory for space manipulator. These joint inter-knot parameters mainly include joint angle and joint angular velocities. The simulation results test that the proposed multi-objective genetic algorithm has satisfactory performance.

关键词： Space manipulator multi-objective genetic algorithm Optimization

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