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A Mechanism to Improve the Interpretability of Linguistic Fuzzy Systems with Adaptive Defuzzification based on the use of a multi-objective Evolutionary Algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2012年第2期5卷 297-321页

作者： Marquez, Antonio A. Marquez, Francisco A. Peregrin, Antonio Univ Huelva Dept Informat Technol Huelva 21819 Spain

This paper proposes a mechanism that helps improve the interpretability of linguistic fuzzy ruled based systems with common adaptive defuzzification methods. Adaptive defuzzification significantly improves the system accuracy, but introduces weights associated with each rule of the rule base, decreasing the system interpretability. The suggested mechanism is based on three goals: 1) reducing the number of total rules considering that rule weight close to zero can be removed;2) reducing the rules with weights coupled because rules with weights close to one do not need the weight, and 3) reducing rules triggered jointly, all of them by using several metrics and a proposed interpretability index. This is performed using a multi-objective evolutionary algorithm, obtaining a set of solutions with different trade-offs between accuracy and interpretability. In addition, it is important to note that adaptive defuzzification and therefore the proposal developed in this work can be used together with other methodologies to improve system interpretability and accuracy, so it can be viewed as an interesting component.

关键词： Linguistic fuzzy modelling interpretability-accuracy trade-off multi-objective genetic algorithms adaptive defuzzification methods

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A multi-objective approach for the motion planning of redundant manipulators

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APPLIED SOFT COMPUTING 2012年第2期12卷 589-599页

作者： Marcos, Maria da Graca Machado, J. A. Tenreiro Azevedo-Perdicoulis, T. -P. Polytech Inst Porto Dept Elect Engn Inst Engn P-4200072 Oporto Portugal Univ Tras Os Montes & Alto Douro Dept Math Vila Real Portugal

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Redundant manipulators Robots Kinematics multi-objective genetic algorithms Trajectory planning

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The Optimal Allocation of Large-scale Water Resource

The Optimal Allocation of Large-scale Water Resource

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2nd International Conference on Civil Engineering, Architecture and Building Materials (CEABM 2012)

作者： Xu Chen-guang Li Yue-peng N China Inst Water Conservancy & Hydroelect Power Zhengzhou Peoples R China

ISBN: (纸本)9783037854228

Large-scale water resource system with multi-water resources and multi-users consists of several independent supply and drainage network systems. It is hard to solve the problem well of large-scale water resource system with traditional planning method. We model the problem of optimal allocation of water resource using the principles of biological evolution, and eliminate the inferior by judging the optimization degree of each generation individuals, then produce a new generation, thus the optimal allocation of water resource is accomplished through iteration repeated. The disadvantage is that MOGA solution out of the quasi-global optimal solution, rather than the true optimal solution.

关键词： multi-objective genetic algorithms water resource optimal allocation

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multi-objective genetic Algorithm Evaluation in Feature Selection

Multi-objective Genetic Algorithm Evaluation in Feature Sele...

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6th International Conference on Evolutionary multi-Criterion Optimization

作者： Spolaor, Newton Lorena, Ana Carolina Lee, Huei Diana Univ Fed ABC Grp Interdisciplinar Mineracao Dados & Aplicacoes Santo Andre Brazil Univ Estadual Oeste Parana Lab Bioinformat Iguacu Brazil

ISBN: (纸本)9783642198922

Feature Selection may be viewed as a search for optimal feature subsets considering one or more importance criteria. This search may be performed with multi-objective genetic algorithms. In this work, we present an application of these algorithms for combining different filter approach criteria, which rely on general characteristics of the data, as feature-class correlation, to perform the search for subsets of features. We conducted experiments on public data sets and the results show the potential of this proposal when compared to mono-objective genetic algorithms and two popular filter algorithms.

关键词： filter feature selection feature importance measures multi-objective genetic algorithms

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Diversity Improvement of Solutions in multiobjective genetic algorithms Using Pseudo Function Inverses

Diversity Improvement of Solutions in Multiobjective Genetic...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Patnaik, Awhan Behera, L. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781457706530

Diversity improvement methods generally implement niching and fitness sharing schemes. In this work we propose a general principle based on using the inverse mapping from objective space to decision space that allows for the creation of diverse solutions in a direct manner. When analytical forms of objective functions are known, we propose a method of generating set-valued inverse maps, in functional or algorithmic form, which when restricted to the feasible search range yield pseudo inverses of objectives. In the absence of analytical functional forms we propose the use of artificial neural networks(ANNs) in a novel configuration to directly learn the inverse map without network inversion procedures. We implement two diversity creation operators and use them in a standard binary multi-objective genetic algorithm(MOGA) to solve standard bi-objective optimization problems. We also propose a parameter less approach of fixing the number and desirable locations of solutions in sparse regions. Proposed algorithms are compared with NSGA-II and it is shown that the proposed algorithms achieve desired level of diversity in fewer function evaluations compared to NSGA-II.

关键词： multi-objective genetic algorithms Artificial Neural Networks inverse problems optimization function approximation

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A Novel multi objective genetic Algorithm for the Portfolio Optimization

A Novel Multi Objective Genetic Algorithm for the Portfolio ...

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7th International Conference on Intelligent Computing (ICIC)

作者： Bevilacqua, Vitoantonio Pacelli, Vincenzo Saladino, Stefano Politecn Bari Dipartimento Elettrotecn & Elettron Via Orabona 4 I-70125 Bari Italy Univ Foggia Fac Econ I-71100 Foggia Italy SpinOff Polytech Bari eBIS srl elect Busines Secur I-139 Bari Italy

ISBN: (纸本)9783642247279

In this paper we propose a new implementation of a multi objective genetic algorithm that handles constrained problems to approach the financial problem of the portfolio optimization. The objective of the paper is to propose and empirically apply a new multi-objective genetic algorithm for portfolio optimization extending the Markowitz mean-variance model ([1,2] Markowitz, 1952 and 1959). At the end of the paper the obtained results are discussed and compared with non linear other different techniques.

关键词： multi-objective genetic algorithms Pareto curves trade-off constraints problems portfolio optimization

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Simultaneous scheduling of production and maintenance tasks in the job shop

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2011年第13期49卷 3891-3918页

作者： Ben Ali, M. Sassi, M. Gossa, M. Harrath, Y. Fac Sci Gafsa Gafsa 2112 Tunisia Ecole Super Sci & Tech Tunis C3S Tunis 1008 Tunisia Univ Hail Dept Comp Sci & Software Engn Hail Saudi Arabia

This paper deals with the job shop problem of simultaneous scheduling of production operations and preventive maintenance tasks. To solve this problem, we develop an elitist multi-objective genetic algorithm that provides a set of Pareto optimal solutions minimising the makespan and the total maintenance cost. A deep study was made to choose the best encoding, operators, and the different probabilities. Some lower bounds of the adopted criteria are developed. The computational experiments carried out on a set of published instances validate the efficiency of the proposed algorithm.

关键词： scheduling job shop maintenance multi-objective genetic algorithms lower bounds GA parameters

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Application of a multi-objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

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ENVIRONMENTAL MANAGEMENT 2011年第3期48卷 448-461页

作者： Maringanti, Chetan Chaubey, Indrajeet Arabi, Mazdak Engel, Bernard Purdue Univ Dept Earth & Atmospher Sci W Lafayette IN 47907 USA Purdue Univ Dept Agr & Biol Engn W Lafayette IN 47907 USA Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA

Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA-I

关键词： multi-objective genetic algorithms BMP tool Nonpoint source pollution Optimization Atrazine Biofuel

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multi-physics design of microvascular materials for active cooling applications

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JOURNAL OF COMPUTATIONAL PHYSICS 2011年第13期230卷 5178-5198页

作者： Aragon, Alejandro M. Smith, Kyle J. Geubelle, Philippe H. White, Scott R. Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Civil & Environm Engn Dept Urbana IL 61801 USA Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA

This paper describes a framework for the design of microvascular polymeric components for active cooling applications. The design of the embedded networks involves complex and competing objectives that are associated with various physical processes. The optimization tool includes a PDE solver based on advanced finite element techniques coupled to a multi-objective constrained genetic algorithm. The resulting Pareto-optimal fronts are investigated in the optimization of these materials for void volume fraction, flow efficiency, maximum temperature, and surface convection objective functions. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Microvascular materials Active cooling multi-physics optimization multi-objective genetic algorithms

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Comparing the performance of metaheuristics for the analysis of multi-stakeholder tradeoffs in requirements optimisation

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INFORMATION AND SOFTWARE TECHNOLOGY 2011年第7期53卷 761-773页

作者： Zhang, Yuanyuan Harman, Mark Finkelstein, Anthony Mansouri, S. Afshin UCL London WC1E 6BT England Brunel Univ Uxbridge UB8 3PH Middx England

Context: In requirements engineering, there will be many different stake holders. Often the requirements engineer has to find a set of requirements that reflect the needs of several different stake holders, while remaining within budget. objective: This paper introduces an optimisation-based approach to the automated analysis of requirements assignments when multiple stake holders are to be satisfied by a single choice of requirements. Method: The paper reports on experiments using two different multi-objective evolutionary optimisation algorithms with real world data sets as well as synthetic data sets. This empirical validation includes a statistical analysis of the performance of the two algorithms. Results: The results reveal that the Two-Archive algorithm outperformed the others in convergence as the scale of problems increase. The paper also shows how both traditional and animated Kiviat diagrams can be used to visualise the tensions between the stake holders' competing requirements in the presence of increasing budgetary pressure. Conclusion: This paper presented the concept of internal tensioning among multi-stakeholder in requirements analysis and optimisation for the first time. This analysis may be useful in internal negotiations over budgetary allowance for the project. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Pareto optimality multi-objective genetic algorithms Requirements engineering

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