Open Access10.5555/2048476.2048498
The simulation-based multi-objective evolutionary optimization (SIMEON) framework (Work-in-Progress)
Ronald Apriliyanto Halim
- 03 Apr 2011
- pp 169-174
2
TL;DR: In this paper, a framework is developed based on Zeigler's modeling and simulation framework and the phases of an optimization study in operations research, and the test and evaluation on the framework implementation show that the framework successfully meets the desired features.
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Abstract: The combination of simulation and optimization has been successfully applied to solve real-world decision making problems However, many of the frameworks used to define the integration between simulation and optimization lack of transparent and coherent structure This consequently deters the effective use of powerful features the simulation technique by optimization practitioners and vice versa Furthermore, it also hinders the development of simulation-based optimization methods that have a proper balance between the desired features (ie generality, efficiency, high-dimensionality and transparency) This research provides the design of the framework that addresses the knowledge gap above and facilitates the fulfillment of the aforementioned features The proposed framework is developed based on Zeigler's modeling and simulation framework and the phases of an optimization study in operations research Finally, the test and evaluation on the framework implementation show that the framework successfully meets the desired features
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Citations
The case for experiment-oriented computing
Paulo Salem da Silva
- 26 Oct 2018
TL;DR: A general Experiment-Oriented Computing (EOC) approach, orthogonal to other Software Engineering issues, is formulated, through which it is possible to clearly pursue both theoretical and applied research with respect to experimental aspects.
3
On-the-fly verification of discrete event simulations by means of simulation purposes: Extended version
Paulo Salem da Silva,Ana C. V. de Melo +1 more
- 01 Aug 2013
TL;DR: In this article, the authors propose a method for applying on-the-fly verification procedures during discrete event simulations by modeling the simulation as a transition system and the property to be verified as another transition system (explicitly).
2
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A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Muiltiobjective optimization using nondominated sorting in genetic algorithms
N. Srinivas,Kalyanmoy Deb +1 more
TL;DR: Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously are investigated and suggested to be extended to higher dimensional and more difficult multiobjective problems.
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Theory of modeling and simulation
Bernard P. Zeigler,Tag Gon Kim,Herbert Praehofer +2 more
- 01 Jan 1976
TL;DR: In this paper, the authors present a rigorous mathematical foundation for modeling and simulation and provide a comprehensive framework for integrating the various simulation approaches employed in practice, including cellular automata, chaotic systems, hierarchical block diagrams, and Petri nets.
Feature Article: Optimization for simulation: Theory vs. Practice
TL;DR: There is a disconnect between research in simulation optimization--which has addressed the stochastic nature of discrete-event simulation by concentrating on theoretical results of convergence and specialized algorithms that are mathematically elegant--and the recent software developments, which implement very general algorithms adopted from techniques in the deterministic optimization metaheuristic literature.
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Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Mitsuo Gen,Runwei Cheng,Lin Lin +2 more
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TL;DR: Network models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing.
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