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Chromatic Selection - An Oversimplified Approach to multi-objective Optimization 18

18th European Conference on Applications of evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： Squillero, Giovanni Politecn Torino Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9783319165486;9783319165493

This short paper introduces the chromatic selection, a simple technique implementable with few tens of lines of code, that enable handling multi-value fitness functions with a single-objective evolutionary optimizer. The chromatic selection is problem independent, requires no parameter tuning, and can be used as a drop-in replacement for both parent and survival selections. The resulting tool will not be a full-fledged multi-objective optimizer, lacking the ability to manage Pareto fronts, but it will efficiently seek a single, reasonable, compromise solution. In several practical problems, the time saved, both in computation and development, could represent a substantial advantage.

关键词： multi-objective evolutionary algorithm evolutionary optimization Selection scheme

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A computational intelligence approach to system-of-systems architecting incorporating multi-objective optimization 1

A computational intelligence approach to system-of-systems a...

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Conference on Systems Engineering Research

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

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. (C) 2015 The Authors. Published by Elsevier B.V.

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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D-NSGA-II: Dual-Stage Nondominated Sorting Genetic algorithm-II 3rd

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3rd International Conference on Robot Intelligence Technology and Applications

作者： Lee, Ki-Baek Kwang Woon Univ Dept Elect Engn 20 Kwangwoon Ro Seoul South Korea

ISBN: (纸本)9783319168418;9783319168401

This paper proposes a novel multi-objective optimization algorithm, dual-stage nondominated sorting genetic algorithm-II (D-NSGA-II) for many-objective problems. Since the percentage of the nondominated solutions increases exponentially with the increasing number of objectives, just finding the nondominated solutions is not enough for solving many-objective problems. In other words, it is necessary to discriminate more meaningful ones from the other non-dominated solutions by additionally incorporating user preference into the algorithms. The proposed D-NSGA-II can obtain not only user preference oriented, but also diverse nondominated solutions by introducing an additional stage of multi-objective optimization. The second stage employs the corresponding secondary objectives, global evaluation and crowding distance which were proposed in the previous research for representing the user's preference to a solution and the crowdedness around a solution, respectively. To demonstrate the effectiveness of the proposed algorithm, some benchmark functions are tested and the outcomes of the proposed D-NSGA-II and the NSGA-II are empirically compared. Experimental results show that D-NSGA-II properly reflects the user's preference in the optimization process as well as the performance in terms of the diversity and solution quality is competitive with the NSGA-II.

关键词： multi-objective evolutionary algorithm Dual-Stage Nondominated Sorting Genetic algorithm-II User Preference Crowding Distance

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Decentralized Load-Frequency Control of Interconnected Power Systems with SMES Units and Governor Dead Band using multi-objective evolutionary algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2009年第4期4卷 443-450页

作者： Ganapathy, S. Velusami, S. Annamalai Univ Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

This paper deals with the design of decentralized controller for load-frequency control of interconnected power systems With Superconducting magnetic energy storage units and Governor Dead Band Nonlinearity using multi-objective evolutionary algorithm. The superconducting magnetic energy storage unit exhibits favourable damping effects by Suppressing the frequency oscillations as well as stabilizing the inter-area oscillations effectively. The proposed control strategy is mainly based on a compromise between Integral Squared Error and Maximum Stability Margin criteria. Analysis on a two-area interconnected thermal power system reveals that the proposed controller improves the dynamic performance of the system and guarantees good closed-loop stability even in the presence of nonlinearities and with parameter changes.

关键词： Governor Dead Band Nonlinearity Load-Frequency Control multi-objective evolutionary algorithm Power Systems Superconducting Magnetic Energy Storage

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Leveraging Human Insights by Combining multi-objective Optimization with Interactive Evolution

Leveraging Human Insights by Combining Multi-Objective Optim...

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作者： Joshua R. Christman Air Force Institute of Technology

学位级别：硕士

Deceptive fitness landscapes are a growing concern for evolutionary computation. Recent work has shown that combining human insights with short-term evolution has a synergistic effect that accelerates the discovery of solutions. While humans provide rich insights, they fatigue easily. Previous work reduced the number of hu- man evaluations by evolving a diverse set of candidates via intermittent searches for novelty. While successful at evolving solutions for a deceptive maze domain, this ap- proach lacks the ability to measure what the human evaluator identifies as important. The key insight here is that multi-objective evolutionary algorithms foster diversity, serving as a surrogate for novelty, while measuring user preferences. This approach, called Pareto Optimality-Assisted Interactive evolutionary Computation (POA-IEC), allows users to identify candidates that they feel are promising. Experimental results reveal that POA-IEC finds solutions in fewer evaluations than previous approaches, and that the non-dominated set is significantly more novel than the dominated set. In this way, POA-IEC simultaneously leverages human insights while quantifying their preferences.

关键词： evolutionary computation interactive evolutionary computation human-led search fitness pareto pareto front multi-objective multi-objective evolutionary algorithm non-dominated set deception non-objective search novelty search stepping stones serendipitous discovery

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Design of the High-sensitivity RFID Sensor Tag with MOEA/D-DE

Design of the High-sensitivity RFID Sensor Tag with MOEA/D-D...

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International Symposium on Antennas and Propagation

作者： Xiaotian Song Gang Wang Yuxing He Department of Electronic Engineering and Information Science University of Science and Technology of China

ISBN: (纸本)9781467379601

The MOEA/D-DE (multi-objective evolutionary algorithm based on decomposition combined with differential evolution) is firstly applied to design a high-sensitivity RFID sensor tag with the consideration of its communication performance. For demonstration, an RFID temperature sensor tag is designed and tested. Both simulated and measured results show the designed sensor tag achieves a three times higher sensing sensitivity and a better communication performance than the one in the literature.

关键词： RFID sensor tag Sensitivity Temperature sensing multi-objective evolutionary algorithm

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multi-objective evolutionary for synthetic aperture radar image segmentation with non-local means denoising

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NATURAL COMPUTING 2014年第1期13卷 39-53页

作者： Li, Yangyang Wei, Ying Wang, Yang Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

Synthetic aperture radar (SAR) image segmentation is an important problem of the realm of image segmentation. In this study, a novel SAR image segmentation algorithm using a multi-objective evolutionary algorithm based on decomposition with non-local means denoising (MISD) is proposed. The novelty of MISD lies in the following issues: (1) an effective multi-objective method with decomposition to solve SAR image segmentation;(2) in order to denoise the SAR images and retain the details, we employ non-local means to remove the noise. The multi-objective decomposition method makes MISD have lower computational complexity. In order to evaluate the performance of the new method, we compared the results with three other popular segmentation approaches on four simulated and two real SAR images. In our experiments, the new method can always find better results, which means MISD is a promising SAR image segmentation method.

关键词： multi-objective evolutionary algorithm Clustering SAR image segmentation Non-local means Decomposition approach

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An evolutionary based dynamic energy management framework for IP-over-DWDM networks

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2014年第2期4卷 94-105页

作者： Chen, Xin Phillips, Chris Queen Mary Univ London Sch Elect Engn & Comp Sci Mile End Rd London E1 4NS England

The energy consumption of the Internet accounts for approximately 1% of the world's total electricity usage, which may become one of the main constraints on its further growth. In response, we propose an evolutionary based dynamic energy management framework that reduces the overall energy consumption without degrading network performance. The main concept is to combine infrastructure sleeping with virtual router migration. During off-peak hours, the virtual routers are moved onto fewer physical platforms and the unused resources are placed in a sleep state to save energy. The sleeping physical platforms are then reawakened during busy periods. In particular, an evolutionary based algorithm called MOEA_VRM is developed to determine where to move the virtual routers in question. The algorithm is then evaluated using a multi-layer fluid flow event-driven simulator to assess its potential. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Energy efficiency IP-over-DWDM core networks multi-objective evolutionary algorithm

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An experimental analysis of the p-median problem under uncertainty: an evolutionary algorithm approach

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2014年第4期8卷 554-578页

作者： Lopez-Monzalvo, Francisco Brizuela, Carlos A. CICESE Res Ctr Ensenada 22860 Baja California Mexico Univ Caribe Dept Ciencias Basicas & Ingn Benito Juarez 77528 Quintana Roo Mexico

Facility location under uncertain environments is an important and challenging problem. The problem deals with the optimal placement of facilities that serve a set of spatially distributed nodes. One way to deal with this problem is to model uncertainty by means of scenarios and to optimise some robustness criteria such as the average and maximum regrets over these scenarios. We propose to model the robust design as a bi-objective optimisation problem and to use a well-known multi-objective evolutionary algorithm, the NSGA-II, to solve it. We also propose to use the bi-objective optimisation framework to analyse the effects of variations in the number of facilities to install, and of nodes to be served, on the quality of the Pareto solutions. Computational experiments show that the proposal can be used to design robust solutions and to study the effects of changes in the system parameters on the quality of the generated solutions.

关键词： p-median problem PMP uncertainty scenarios robustness regret multi-objective evolutionary algorithm MOEA

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multi-objective Optimization Model with AHP Decision-making for Cloud Service Composition

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2015年第9期9卷 3293-3311页

作者： Liu, Li Zhang, Miao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

关键词： multi-Cloud Services Composition multi-objective evolutionary algorithm AHP

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