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2008 Design, Automation and Test in Europe (DATE 2008), vol.1

作者： Sawal Ali Reuben Wilcock Peter Wilson Andrew Brown University of Southampton UK

ISBN: (纸本)9783981080131

A new algorithm is presented that combines performance and variation objectives in a behavioural model for a given analogue circuit topology and process. The tradeoffs between performance and yield are analysed using a combination of a multi-objective evolutionary algorithm and Monte Carlo simulation. The results indicate a significant improvement in overall simulation time and efficiency compared to conventional simulation based approaches, without a corresponding drop in accuracy. This approach is particularly useful in the hierarchical design of large and complex circuits where computational overheads are often prohibitive. The behavioural model has been developed in Verilog-A and tested extensively with practical designs using the Spectre simulator. A benchmark OTA circuit was used to demonstrate the proposed algorithm and the behaviour has been verified with transistor level simulations of this circuit and a higher level filter design. This has demonstrated that an accurate performance and yield prediction can be achieved using this model, in a fraction of the time of conventional simulation based methods.

关键词： Analog integrated circuits Behavioural circuit complexity layer design multi-objective evolutionary algorithm simulating Monte Carlo technique behavioral models analog circuits

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Reliability-based optimization for multiple constraints with evolutionary algorithms

Reliability-based optimization for multiple constraints with...

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IEEE Congress on evolutionary Computation

作者： Daum, David A. Deb, Kalyanmoy Branke, Juergen Univ Karlsruhe Inst AIFB Karlsruhe Germany

ISBN: (纸本)9781424413393

In this paper, we combine reliability-based optimization with a multi-objective evolutionary algorithm for handling uncertainty in decision variables and parameters. This work is an extension to a previous study by the second author and his research group to more accurately compute a multi-constraint reliability. This means that the overall reliability of a solution regarding all constraints is examined, instead of a reliability computation of only one critical constraint. First, we present a brief introduction into this so-called 'structural reliability' aspects. Thereafter, we introduce a method for identifying inactive constraints according to the reliability evaluation. With this method, we show that with less number of constraint evaluations, an identical solution can be achieved. Furthermore, we apply our approach to a number of problems including a real-world car side impact design problem to illustrate our method.

关键词： evolutionary algorithm reliability evaluation Constraint multi-objective evolutionary algorithm Side impact

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A new optimal dispatch method for the day-ahead electricity market using a multiobjective evolutionary approach

A new optimal dispatch method for the day-ahead electricity ...

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39th North American Power Symposium

作者： Li, Dapeng Pahwa, Anil Das, Sanjoy Rodrigo, Deepal Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66506 USA Midwest ISO Inc Carmel IN 46032 USA

ISBN: (纸本)9781424417254

In this paper, a novel approach to optimally operate a day-ahead electricity market with considerations of market power is proposed. First, this approach formulates the 1-hour based optimal dispatch problem as a nonlinear constrained multi-objective optimization problem which simultaneously minimizes fuel cost, emissions and the modified Herfindahl-Hirschman index. Then the Non-dominated Sorting Genetic algorithm II (NSGA-II) is used to solve this optimization problem. Simulation results illustrate that this algorithm is capable of finding the Pareto-optimal front in a single run. The best operation schedule for the whole 24 hours can be chosen by taking the ramp rate constraints of generators into account. This approach can provide the market operator with much operating feasibility and thus is more realistic and feasible.

关键词： economic dispatch market power day-ahead electricity market multi-objective evolutionary algorithm

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Constraint handling in multi-objective evolutionary optimization

Constraint handling in multi-objective evolutionary optimiza...

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IEEE Congress on evolutionary Computation

作者： Woldesenbet, Yonas G. Tessema, Birak G. Yen, Gary G. Oklahoma State Univ Stillwater OK 74078 USA

ISBN: (纸本)9781424413393

This paper introduces a new constraint handling technique for multi-objective evolutionary algorithms based on adaptive penalty functions and distance measures of an individual. These two values are used to modify the objective space. The modified objective functions are used in the non-dominance sorting so that the algorithm evolves feasible optimal solutions not only from the feasible space but also from the infeasible space. The search in the infeasible space is designed to encourage those individuals with better objective value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible solutions or toward finding optimum solutions. The proposed method is simple to implement and does not need any parameter tuning. The constraint handling technique was tested on several constrained multi-objective problems and has shown superior results.

关键词： constraint handling Search process multi-objective evolutionary algorithm handling techniques penalty function

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Scheduling flexible manufacturing systems using parallelization of multi-objective evolutionary algorithms

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第3-4期30卷 279-285页

作者： Sankar, S. Saravana Ponnambalam, S. G. Gurumarimuthu, M. Arulmigu Kalasalingam Coll Engn Dept Mech Engn Krishnankoil 626190 India Monash Univ Selangor 46150 Malaysia

Solving multi-objective scientific and engineering problems is, generally, a very difficult goal. In these optimization problems, the objectives often conflict across a high-dimensional problem space and require extensive computational resources. In this paper, a migration model of parallelization is developed for a genetic algorithm (GA) based multi-objective evolutionary algorithm (MOEA). The MOEA generates a near-optimal schedule by simultaneously achieving two contradicting objectives of a flexible manufacturing system (FMS). The parallel implementation of the migration model showed a speedup in computation time and needed less objective function evaluations when compared to a single-population algorithm. So, even for a single-processor computer, implementing the parallel algorithm in a serial manner (pseudo-parallel) delivers better results. Two versions of the migration model are constructed and the performance of two parallel GAs is compared for their effectiveness in bringing genetic diversity and minimizing the total number of functional evaluations.

关键词： multi-objective evolutionary algorithm genetic algorithm parallelization scheduling optimization flexible manufacturing system

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Comparison of multi-objective evolutionary algorithms in optimizing combinations of reinsurance contracts 06

Comparison of multi-objective evolutionary algorithms in opt...

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8th Annual Genetic and evolutionary Computation Conference

作者： Oesterreicher, Ingo Mitschele, Andreas Schlottmann, Frank Seese, Detlef Univ Karlsruhe TH Inst AIFB D-76128 Karlsruhe Germany GILLARDON AG Financial Software D-75015 Bretten Germany

ISBN: (纸本)9781595931863

Our paper concerns optimal combinations of different types of reinsurance contracts. We introduce a novel approach based on the Mean-Variance-Criterion to solve this task. Two state-of-the-art MOEAs are used to perform an optimization of yet unresolved problem instances. In addition to that, we focus on finding a dense set of solutions to derive analogies to theoretic results of easier problem instances.

关键词： multi-objective evolutionary algorithm optimal reinsurance mean-variance-criterion value-at-risk

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Pareto efficient multi-objective test case selection 07

Pareto efficient multi-objective test case selection

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Proceedings of the 2007 international symposium on Software testing and analysis

作者： Shin Yoo Mark Harman King's College London

ISBN: (纸本)9781595937346

Previous work has treated test case selection as a single objective optimisation problem. This paper introduces the concept of Pareto efficiency to test case selection. The Pareto efficient approach takes multiple objectives such as code coverage, past fault-detection history and execution cost, and constructs a group of non-dominating, equivalently optimal test case subsets. The paper describes the potential bene?ts of Pareto efficient multi-objective test case selection, illustrating with empirical studies of two and three objective formulations.

关键词： multi-objective evolutionary algorithm test case selection

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multi-objective modeling and optimization for cleaner production processes

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JOURNAL OF CLEANER PRODUCTION 2006年第2期14卷 146-151页

作者： Jia, XP Zhang, TZ Wang, F Han, FY Tsinghua Univ Dept Environm Sci & Engn Beijing 100084 Peoples R China Univ Paderborn Inst Math D-33100 Paderborn Germany Qingdao Univ Sci & Technol Ctr Comp & Chem Engn Qingdao 266042 Peoples R China

The paper presents a methodology for multi-objective modeling and optimization with environmental impacts and economics aspects simultaneously in the context of cleaner production. A generalized multi-objective process model is proposed based on a holistic concept of macro-structure. A hybrid multi-objective evolutionary algorithm is proposed to solve this multi-objective optimization problem. The Elitist Non-dominated Sorting Genetic algorithm is used to obtain the Pareto-optimal set. The best compromised solution is determined by using the Technique for Order Preference by Similarity to the Ideal Solution. A simple reaction system is presented as a case study to illustrate the proposed methodology. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： process optimization environmental impacts multi-objective evolutionary algorithm cleaner production

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A multi-objective genetic algorithm with controllable convergence on knee regions

A multi-objective genetic algorithm with controllable conver...

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IEEE Congress on evolutionary Computation

作者： Rachmawati, L. Srinivasan, D. Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore

ISBN: (纸本)9780780394872

A knee region on the Pareto-optimal front of a multi-objective optimization problem consists of solutions with the maximum marginal rates of return, i.e. solutions for which an improvement on one objective is accompanied by a severe degradation in another. The trade-off characteristic renders such solutions of particular interest in practical applications. This paper presents a multi-objective evolutionary algorithm focused on the knee regions. The algorithm facilitates better decision making in contexts where high marginal rates of return are desirable by providing the Decision Makers with a high concentration of solutions on the knee regions of the Pareto-front approximation. The proposed approach computes a transformation of the original objectives based on weighted-sum functions. The transformed functions identify niches which correspond to knee regions in the objective space. The extent and density of coverage of the knee regions are controllable by the niche strength and pool size parameters. Although based on weighted-sums, the algorithm is capable of finding solutions in the non-convex regions of the Pareto-front. The application of the algorithm on test problems with multiple knee regions and skew on the Pareto-optimal front produces promising results.

关键词： Knee region knee niches algorithms multi-objective evolutionary algorithm decision makers test problem Skew

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An algorithm for traffic grooming in WDM optical mesh networks with multiple objectives

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TELECOMMUNICATION SYSTEMS 2005年第3-4期28卷 369-386页

作者： Prathombutr, P Stach, J Park, E Univ Missouri Sch Interdisciplinary Comp & Engn Kansas City MO 64110 USA

This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting to an optical signal carried in the all-optical network. We design a virtual topology with a given physical topology to satisfy multiple objectives and constraints. The grooming problem of a static demand is considered as an optimization problem. The traditional algorithms found in the literatures mostly focus on a single objective either to maximize the performance or to minimize the cost. We propose a multi-objective evolutionary algorithm to solve a grooming problem that optimizes multiple objectives all together at the same time. In this paper we consider the optimization of three objectives: maximize the traffic throughput, minimize the number of transceivers, and minimize the average propagation delay or average hop counts. The simulation results show that our approach is superior to an existing heuristic approaches in an acceptable running time.

关键词： multi-objective evolutionary algorithm grooming WDM mesh optical networks Pareto optimal

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