Pareto Optimality and Particle Swarm Optimization

Baumgartner, Ulrike; Magele, Christian; Renhart, Werner

doi:10.1109/tmag.2004.825430

Cited by 138 publications

(68 citation statements)

References 3 publications

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“…As can be noted, the formulation of the problem leads to solutions which try to 'follow' the leader's x gbest position as well as attracting solutions versus the personal best solution of the particle x pbesti . So far, several approaches have been proposed for extending the formulation of the PSO technique to the multiobjective domain [13,2]. Here we propose a technique based on an "aggregating" approach where the swarm is equally partitioned in n subswarms, each of which uses a different cost-function which is the product of the objectives combined with a set of exponents randomly chosen.…”

Section: Proposed Particle Swarm Optimizationmentioning

confidence: 99%

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Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration

Palermo

Silvano

Zaccaria

2008

2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools

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Platform-based design represents the most widely used approach to design System-On-Chip (SOC) applications. In this context, the Design Space Exploration (DSE) phase consists of optimally configure a parameterized SOC platform in terms of system-level requirements depending on the target application. In this paper, we introduce the Discrete Particle Swarm Optimization methodology (DPSO) for supporting the DSE of an hardware platform. The proposed technique aims at efficiently profiling the target application and deriving an approximated Pareto set of system configurations with respect to the selected figures of merit. Once the approximated Pareto set has been built, the designer can quickly select the best system configuration satisfying the constraints. Experimental results show that the proposed DPSO technique can speed up the design space exploration time up to 5X with an accuracy of up to 70% with respect to a full search exploration for the selected benchmarks 1 .

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Section: Proposed Particle Swarm Optimizationmentioning

confidence: 99%

“…It can be shown that solutions to Problem 1 lie on the Pareto surface of the original problem. The approach presented in [2] uses, instead, a linear combination of cost functions {f 1 . .…”

Section: Proposed Particle Swarm Optimizationmentioning

confidence: 99%

Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration

Palermo

Silvano

Zaccaria

2008

2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools

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“…Baumgartner (2004) proposed particle swarm optimization which imitates the social behavior of birds in a flock flying around and sitting down on a pylon. Yoon (2005) describes a tool to generate way finding aids in dynamic virtual worlds in an ant colony.…”

Section: Background Multi-agent Systemsmentioning

confidence: 99%

SimCenter Hawaii: Virtual Reality Applications for Health Care Education and Training

Burgess¹

2008

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 5f. WORK UNIT NUMBER PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) 17-12-2008 REPORT TYPE Final DATES COVERED (From -To PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORTUCERA 651 Ilalo Street, MEB 224N Honolulu, HI 96813 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)U.S. Army Medical Research Fort Detrick, MD 21702-5012 SPONSOR/MONITOR'S REPORT NUMBER(S)12. DISTRIBUTION / AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES N/A 14. ABSTRACT Technology-Enabled Learning and Intervention Systems (TELIS) describes a development model and associated tools for advanced educational products and applications. As an extension of this roadmap, SimCenter Hawaii has developed a research design model that addresses specific challenges (e.g., improved user interfaces, the development of telecollaboration tools) directed toward improving the development of effective TELIS educational applications that can benefit the military's training programs. The following 3 medical simulation projects are herein proposed under the SimCenter Hawaii's TELIS model: 1) Implementing a combination of usability engineering methods, an evaluation model that combines two iterations of heuristic usability evaluations and one iteration of user testing, will achieve measurable software design improvements in development of the VR nephron; 2) Didactic training plus VR Triage training will result in improved triage skills acquisition over didactic training alone; and 3) the Virtual Reality Motor-Skills Simulator will result in equal or better results than a box trainer in developing fine-motor surgical skills, as assessed by performance of a virtual procedure on the LapSim simulator. SUBJECT TERMS IntroductionCurrently, technology has evolved to potentially transform learning systems, however; to achieve effective learning tools, great effort must be taken to develop, test and disseminate the technology.(3) A systematic, reproducible approach to research and development must be implemented so that results produce usable, validated, learning systems. SimC...

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“…Liu [70] use a modified fuzzy-Chebyshev programming (MFCP) to generate the weight or quantify the level of importance for each objective based on its satisfaction level. Marandi [71] applied an ordered weighted averaging operator to transform a 3-objectives MO problem to a one objective cost function and a Mamdani fuzzy inference system to calculate weights for objectives. Baumgartner [36,40], adopted a gradient technique based approach to the weights adjustment.…”

Section: Weighted Aggregation Approachmentioning

confidence: 99%

RhoGTPase Involvement in Breast Cancer Migration and Invasion

Simpson¹

2008

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Pareto Optimality and Particle Swarm Optimization

Cited by 138 publications

References 3 publications

Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration

Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration

SimCenter Hawaii: Virtual Reality Applications for Health Care Education and Training

RhoGTPase Involvement in Breast Cancer Migration and Invasion

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