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A Computational Intelligence Approach to System-of-Systems Architecting Incorporating multi-objective Optimization

Procedia Computer Science 2015年 44卷 86-94页

作者： David M. Curry Cihan H. Dagli Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla Missouri 65409

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization.

关键词： System-of-Systems SoS Architecting Genetic algorithm GA multi-objective Optimization Problem MOP multi-objective evolutionary algorithm MOEA Meta-Architecture

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multi-objective multiagent Credit Assignment in NSGA-II Using Difference Evaluations 15

Multi-Objective Multiagent Credit Assignment in NSGA-II Usin...

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International Conference on Autonomous Agents and multiagent Systems

作者： Logan Yliniemi Drew Wilson Kagan Tumer Oregon State University Austin Peay State University

ISBN: (纸本)9781450337717

Determining the contribution of an agent to a system-level objective function (credit assignment) is a key area of research in cooperative multiagent systems. multi-objective optimization is a growing area of research, though mostly focused on single agent settings. Many real-world problems are multiagent and multi-objective, (e.g., air traffic management, scheduling observations across multiple exploration robots) yet there is little work on their intersection. In this work, we leverage recent advances in single-objective multiagent learning to address multi-objective domains. We focus on the impact of difference evaluation functions (which extracts an agent's contribution to the team objective) on the Non-dominated Sorting Genetic algorithm-II (NSGA-II), a state-of-the-art multi-objective evolutionary algorithm. We derive multiple methods for incorporating difference evaluations into the NSGA-II framework, and test each in a multiagent rover exploration domain, which is a good surrogate for a wide variety of distributed scheduling and resource gathering problems. We show that how and where difference evaluations are incorporated in the NSGA-II algorithm is critical, and can either provide significant benefits or destroy system performance, depending on how it is used. Median performance of the correctly used difference evaluations dominates best-case performance of NSGA-II in a multiagent multi-objective problem.

关键词： multiagent Learning multi-objective Problem multi-objective evolutionary algorithm NSGA-II

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A Fuzzy multi-objective Portfolio Selection Model with Transaction Cost and Liquidity

A Fuzzy Multi-objective Portfolio Selection Model with Trans...

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International Conference on Computational Intelligence and Security

作者： Wei Yue Yuping Wang School of Computer Science and Technology Xidian University Xi'an Shannxi China

In this paper, a new proportion entropy function is proposed as an objective function to obtain a well-diversified portfolio. Secondly, a new fuzzy multi-objective portfolio selection model based on the proposed entropy function is presented. By using the model, we can find tradeoffs between risk, return and the diversification degree of portfolio. Thirdly, a new multi-objective evolutionary algorithm is designed to solve the proposed model. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed model and the corresponding algorithm.

关键词： Entropy multi-objective evolutionary algorithm Portfolio selection Possibilistic moments multi-objective evolutionary algorithm entropy entropy function liquidity Fuzzy tradeoffs objective function

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multi-objective vs. Single-objective evolutionary algorithms for Hybrid Mobile Robot Optimization

Multi-Objective vs. Single-Objective Evolutionary Algorithms...

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IEEE International Symposium on Robotics and Manufacturing Automation (IEEE-ROMA)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479957651

In this paper, the comparison of multi-objective evolutionary algorithm (MOEA) and Single-objective evolutionary algorithm (SOEA) in designing and optimizing the morphology of a Six Articulated-Wheeled Robot (SAWR) is presented. Results show that both methods are able to produce optimized SAWR which have smaller size with the capability to perform climbing motion. However, one of the solutions from the Pareto-set of MOEA is outperforming the fittest solution from SOEA. The solution is able to achieve the same performance of the fittest solution from SOEA and yet it is smaller in size. Besides that, another advantage of using MOEA is that MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance which provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm single-objective evolutionary algorithm evolutionary robotics evolved hybrid mobile robot

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multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis

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NEUROCOMPUTING 2014年 146卷 113-124页

作者： Xia, Hu Zhuang, Jian Yu, Dehong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

The multi-objective evolutionary algorithm (MOEA) has shown remarkable capability of selecting feature subset. Most MOEAs use the cardinality of the feature subset as one of its objectives and adopt a strict Pareto dominance relationship to select individuals. However, these techniques limit available solutions and may omit several appropriate but dominated solutions. A multi-objective unsupervised feature selection algorithm (MOUFSA) is proposed to solve these issues. A new objective, which incorporates the correlation coefficient and cardinality of the feature subset, not only evaluates the redundancy of selected features but also provides several objective values for each particular size of feature subset. A relaxed archiving strategy based on negative epsilon-dominance and the box-based method is designed to preserve promising solutions even if they are dominated. Three new mutation operators of different abilities are also presented to enhance the algorithm. Nine UCI datasets and five fault recognition datasets are employed as test objects, and the obtained feature subsets are then used for subsequent classification and clustering. Experimental results show that MOUFSA outperforms several other multi-objective and traditional single-objective methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Feature selection Redundancy measure Negative epsilon-dominance Fault recognition

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A new multi-objective multi-mode model for solving preemptive time-cost-quality trade-off project scheduling problems

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第4期41卷 1830-1846页

作者： Tavana, Madjid Abtahi, Amir-Reza Khalili-Damghani, Kaveh La Salle Univ Business Syst & Analyt Dept Lindback Distinguished Chair Informat Syst & Deci Philadelphia PA 19141 USA Univ Paderborn Fac Business Adm & Econ Business Informat Syst Dept D-33098 Paderborn Germany Univ Econ Sci Dept Knowledge Engn & Decis Sci Tehran Iran Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Considering the trade-offs between conflicting objectives in project scheduling problems (PSPs) is a difficult task. We propose a new multi-objective multi-mode model for solving discrete time-cost-quality trade-off problems (DTCQTPs) with preemption and generalized precedence relations. The proposed model has three unique features: (1) preemption of activities (with some restrictions as a minimum time before the first interruption, a maximum number of interruptions for each activity, and a maximum time between interruption and restarting);(2) simultaneous optimization of conflicting objectives (i.e., time, cost, and quality);and (3) generalized precedence relations between activities. These assumptions are often consistent with real-life projects. A customized, dynamic, and self-adaptive version of a multi-objective evolutionary algorithm is proposed to solve the scheduling problem. The proposed multi-objective evolutionary algorithm is compared with an efficient multi-objective mathematical programming technique known as the efficient epsilon-constraint method. The comparison is based on a number of performance metrics commonly used in multi-objective optimization. The results show the relative dominance of the proposed multi-objective evolutionary algorithm over the epsilon-constraint method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Project scheduling problem Discrete time-cost trade-off problem multi-objective evolutionary algorithm Activity preemption Generalized precedence relations

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A multi-objective evolutionary Approach to Optimize the Morphology of a Six Articulated-Wheeled Robot 4

A Multi-Objective Evolutionary Approach to Optimize the Morp...

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4th IEEE International Conference on Artificial Intelligence with applications in Engineering and Technology (ICAIET)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479979103

This paper proposed a multi-objective evolutionary algorithm (MOEA) in designing the morphology of a six articulated-wheeled robot (SAWR) which has the ability to perform climbing motion. The first objective is to minimize the morphology design while the second objective is to maximize the performance of the SAWR in performing the climbing motion. Results show that the proposed MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance. The Pareto set of optimal solutions provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm hybrid mobile robot optimized hybrid mobile robot

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An integrated cultural particle swarm algorithm for multi-objective reliability-based design optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2014年第7期228卷 1185-1196页

作者： Li, Zhongkai Tian, Guangdong Cheng, Gang Liu, Houguang Cheng, Zhihong China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Northeast Forestry Univ Transportat Coll Harbin Peoples R China

Uncertainties in design variables and problem parameters are often inevitable in multi-objective optimizations, and they must be considered in an optimization task if reliable Pareto optimal solutions are to be sought. multi-objective reliability-based design optimization has been raised as a question in design for reliability, but the disadvantages of fixed evolutionary parameters, nonuniformly distributed Pareto optimal solutions and high computational cost hinder engineering applications of reliability-based design. To deal with it, this work proposes an integrated multi-objective cultural-based particle swarm algorithm to solve the double-loop reliability-based design optimization. In the inner optimization loop, the cultural space is composed of the elitism, situational and normative knowledge to adjust the parameters for swarm space, and the crowding distance ranking is introduced to update the global and local optimum and control the maximum number of solutions in elitism knowledge. The hybrid mean value method is improved to perform reliability analysis in the outer loop to suit both concave and convex types of performance functions. In addition, the car side-impact and the injection molding machine are chosen as multi-objective reliability design examples to demonstrate the effectiveness of the proposed approach. Simultaneously, results of car side-impact problem are compared with two traditional multi-objective reliability optimization algorithms, i.e., nondominated sorting genetic algorithm and crowding distance ranking-based multi-objective particle swarm optimizer, to assess the efficiency of the proposed approach. The results denote the proposed cultural-based multi-objective particle swarm optimizer is effective and feasible to solve the reliability-based design optimization problems.

关键词： reliability analysis cultural algorithm Reliability-based design optimization multi-objective evolutionary algorithm particle swarm optimization

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Adaptive arm motion generation of humanoid robot operating in dynamic environments

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2014年第2期41卷 124-134页

作者： Mohamed, Zulkifli Kitani, Mitsuki Capi, Genci Toyama Univ Dept Elect & Elect Syst Engn Toyama 930 Japan Univ Teknol MARA Fac Mech Engn Shah Alam Selangor Malaysia

Purpose - The purpose of this paper is to compare the performance of the robot arm motion generated by neural controllers in simulated and real robot experiments. Design/methodology/approach - The arm motion generation is formulated as an optimization problem. The neural controllers generate the robot arm motion in dynamic environments optimizing three different objective functions;minimum execution time, minimum distance and minimum acceleration. In addition, the robot motion generation in the presence of obstacles is also considered. Findings - The robot is able to adapt its arm motion generation based on the specific task, reaching the goal position in simulated and experimental tests. The same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions. Research limitations/implications - The motion generated yield good results in both simulation and experimental environments. Practical implications - The robot motion is generated based on three different objective functions that are simultaneously optimized. Therefore, the humanoid robot can perform a wide range of tasks in real-life environments, by selecting the appropriate motion. Originality/value - A new method for adaptive arm motion generation of a mobile humanoid robot operating in dynamic human and industrial environments.

关键词： Arm motion generation Mobile humanoid robot multi-objective evolutionary algorithm

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Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

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INFORMATION SCIENCES 2014年 258卷 160-181页

作者： Basgalupp, Marcio P. Barros, Rodrigo C. de Carvalho, Andre C. P. L. F. Freitas, Alex A. Univ Fed Sao Paulo BR-12231280 Sao Jose Dos Campos SP Brazil Univ Sao Paulo BR-13560970 Sao Carlos SP Brazil Univ Kent Canterbury CT2 7NF Kent England

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Decision tree Lexicographic optimization Machine learning multi-objective evolutionary algorithm

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