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5th International Conference on Machine Learning and Cybernetics

作者： Xing, Zong-Yi Hou, Yuan-Long Zhang, Yong Jia, Li-Min Gao, Qiang Nanjing Univ Sci & Technol Sch Mech Engn Jiangsu 210094 Peoples R China Beijing Jiaotong Univ Sch Transportat Beijing 100044 Peoples R China

ISBN: (纸本)1424400600

An approach to construct interpretable and precise fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. Then a modified fuzzy clustering algorithm, combined with the least square method, is used to identify the initial fuzzy model. Third, the multi-objective genetic algorithm and interpretability-driven simplification techniques are proposed to evolve the initial fuzzy model to optimize its structure and parameters iteratively, thus interpretability and precision of the fuzzy model are improved. Finally, the proposed approach is applied to the Mackey-Glass tine series, and the results show its validity.

关键词： fuzzy modeling fuzzy clustering multi-objective genetic algorithm TS fuzzy model interpretability

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Optimal multi-objective linearized impulsive rendezvous under uncertainty

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ACTA ASTRONAUTICA 2010年第3-4期66卷 439-445页

作者： Li, Hai-yang Luo, Ya-Zhong Jin-Zhang Tang, Guo-Jin Natl Univ Def Technol Coll Aerosp & Mat Engn Changsha 410073 Hunan Peoples R China

The multi-objective optimization of linearized impulsive rendezvous under uncertainty is investigated in this paper. A performance index related to the variances of the terminal state error is defined as the robustness performance index, which is determined by the linear covariance method. The three-objective of the total characteristic velocity, the rendezvous time of flight, and the robustness performance index optimization model based on the Clohessy-Wiltshire (C-W) system is proposed. The multi-objective nondominated sorting genetic algorithm is employed to obtain the Pareto solution set. The optimization results of one homing rendezvous mission are provided, demonstrating that tradeoffs between time of flight, propellant cost and trajectory robustness and several inherent principles of the rendezvous trajectory can be quickly obtained via the current approach. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Rendezvous Uncertainty Robust optimization Linear covariance multi-objective genetic algorithm

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A multi-objective particle swarm optimization for project selection problem

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 315-321页

作者： Rabbani, M. Bajestani, M. Aramoon Khoshkhou, G. Baharian Univ Tehran Dept Ind Engn Tehran 4563 Iran

Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this Study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： multi-objective particle swarm Project selection problem multi-objective genetic algorithm

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A market-oriented dynamic collaborative cloud services platform

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ANNALS OF TELECOMMUNICATIONS 2010年第11-12期65卷 669-688页

作者： Hassan, Mohammad Mehedi Song, Biao Huh, Eui-Nam Kyung Hee Univ Dept Comp Engn Internet Comp & Network Secur Lab Yongin 446701 Gyeonggi Do South Korea

Currently, interoperability and scalability are two major challenging issues for cloud computing. Forming a dynamic collaboration (DC) platform among cloud providers (CPs) can help to better address these issues. A DC platform can facilitate expense reduction, avoiding adverse business impacts and offering collaborative or portable cloud services to consumers. However, there are two major challenges involved in this undertaking;one is to find an appropriate market model to enable a DC platform, and the other one is to minimize conflicts among CPs that may occur in a market-oriented DC platform. In this paper, we present a novel combinatorial auction (CA)-based cloud market (CACM) model that enables a DC platform in CPs. To minimize conflicts among CPs, a new auction policy is proposed that allows a CP to dynamically collaborate with suitable partner CPs to form groups and publishes their group bids as a single bid to compete in the auction. However, identifying a suitable combination of CP partners to form the group and reduce conflicts is a NP-hard problem. Hence, we propose a promising multi-objective (MO) optimization model for partner selection using individual information and past collaborative relationship information, which is seldom considered. A multi-objective genetic algorithm (MOGA) called MOGA-IC is proposed to solve the MO optimization problem. This algorithm is developed using two popular MOGAs, the non-dominated sorting genetic algorithm (NSGA-II) and the strength pareto evolutionary genetic algorithm (SPEA2). The experimental results show that MOGA-IC with NSGA-II outperformed the MOGA-IC with SPEA2 in identifying useful pareto-optimal solution sets. Other simulation experiments were conducted to verify the effectiveness of the MOGA-IC in terms of satisfactory partner selection and conflict minimization in the CACM model. In addition, the performance of the CACM model was compared to the existing CA model in terms of economic efficiency.

关键词： Cloud market Combinatorial auction Dynamic collaboration Interoperability Partner selection multi-objective genetic algorithm

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An Improved multi-objective Adaptive Niche genetic algorithm Based On Pareto Front

An Improved Multi-Objective Adaptive Niche Genetic Algorithm...

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IEEE International Advance Computing Conference

作者： Zhang, Jingjun Shang, Yanmin Gao, Ruizhen Dong, Yuzhen Hebei Univ Engn Dept Sci Res Handan Peoples R China

ISBN: (纸本)9781424429271

For multi-objective optimization problems, an improved multi-objective adaptive niche genetic algorithm based on Pareto Front is proposed in this paper. In this algorithm, the rank value and the niche value are introduced to evaluate the individuals. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The experimental results show that this algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution.

关键词： Pareto solution Pareto dominate multi-objective genetic algorithm niche genetic algorithm adaptive

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Solving multi-objective routing and wavelength assignment in WDM network using hybrid evolutionary computation approach

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COMPUTER COMMUNICATIONS 2010年第18期33卷 2246-2259页

作者： Leesutthipornchai, Pakorn Charnsripinyo, Chalermpol Wattanapongsakorn, Naruemon King Mongkuts Univ Technol Thonburi Fac Engn Dept Comp Engn Bangkok 10140 Thailand Natl Elect & Comp Technol Ctr Klongluang 12120 Pathumthani Thailand

Routing and wavelength assignment (RWA) is a well-known issue in wavelength division multiplexing (WDM) optical networks. In this paper, we present RWA design for WDM networks by considering multiple design objectives which are maximizing the number of traffic demands to be served and minimizing the number of wavelength channels to be assigned. A hybrid evolutionary computation approach consisting of genetic algorithm for routing allocation with minimum degree first for wavelength assignment (GA-MDF) and the fast non-dominated sorting genetic algorithm (NSGA-II) to search for non-dominated solutions is applied for solving the multi-objective RWA network design problem. The hybrid evolutionary algorithm is used as a meta-heuristic technique for obtaining good solutions for various problem sizes. The obtained results are provided as candidate choices or non-dominated front. We compare the simulation results obtained from the NSGA-II with those obtained from the traditional Weighted-Sum approach. Numerical results show that our hybrid evolutionary computation approach is effective in solving the multi-objective RWA problem. The GA-MDF can outperform the FAR-FF method in both design objectives. In addition, the solutions from the NSGA-II are more diverse on the multi-objective space than those of the Weighted-Sum method. We also apply a Pruned mechanism to help cutting off numerous non-dominated solutions for making decision on the final solution. (C) 2010 Elsevier B.V. All rights reserved.

关键词： multi-objective genetic algorithm Routing and wavelength assignment WDM network

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APPLICATION OF THE multi-objective PARETO-BASED genetic algorithm IN SOLVING GEOSPATIAL OPTIMISATION PROBLEMS

APPLICATION OF THE MULTI-OBJECTIVE PARETO-BASED GENETIC ALGO...

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9th International multidisciplinary Scientific Geo-Conference and Expo

作者： Ponjavic, Mirza Avdagic, Zikrija Karabegovic, Almir Univ Sarajevo Dept Comp Sci & Informat Fac Elect Engn Sarajevo 71000 Bosnia & Herceg Univ Sarajevo Fac Civil Engn Dept Geodesy Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9789549181814

This work studies the development and application of the multi-objective genetic algorithm based on the Pareto approach, as a tool for the decision making support in the geospatial analysis. Pareto-based evolutionary mechanism developed as an approach to multi-objective geospatial optimisation operates with fixed parameters of genetic operators. It can be used as efficient tool for multi-objective planning both for their power and flexibility and the fact that they generate a whole set of good solutions rather than just one "optimal" solution. The implementation of the suggested multi-objective Pareto based genetic algorithm over selected geospatial optimisation problem of fire station location demonstrates its ability of the discovery of multiple compromise solutions in a real spatial problem domain.

关键词： multi-objective genetic algorithm geospatial analysis geoinformation system location-allocation problem

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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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Bottom-up Evolutionary Subspace Clustering

Bottom-up Evolutionary Subspace Clustering

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2010 IEEE World Congress on Computational Intelligence

作者： Vahdat, Ali Heywood, Malcolm Zincir-Heywood, Nur Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

ISBN: (纸本)9781424481262

The ultimate goal of subspace clustering algorithms is to identify both the subset of attributes supporting a cluster and the location of the cluster in the subspace. In this work a generic evolutionary approach to bottom-up subspace clustering is proposed consisting of three steps. The first applies a non-evolutionary clustering algorithm attribute-wise to establish the lattice from which subspace clusters will be designed. In the second step a multi-objective genetic algorithm (MOGA) is used to evolve good candidate subspace clusters (CSC) through a combinatorial search w.r.t. the attribute-wise lattice from step 1. The third step then searches in the space of CSC from the population of the the first MOGA to find the best combination of subspace clusters, again under a MOGA formulation. Important properties of the approach are that a standard clustering algorithm is deployed in step one to build the initial lattice of attribute-wise clusters. This helps to decouple the computational expense of clustering using Evolutionary Computation, with the MOGA applied in steps 2 and 3 building clusters through a combinatorial search relative to the original lattice parameters. Benchmarking on data sets with tens to hundreds of attributes illustrates the feasibility of the approach.

关键词： attribute-wise lattice bottom-up evolutionary subspace clustering candidate subspace clusters combinatorial search genetic algorithms multi-objective genetic algorithm pattern clustering search problems

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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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