Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization

Vintan, Lucian; Chis, Radu; Ismail, Muhammad Ali; Cotofana, Cristian

doi:10.1109/tcad.2015.2501299

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The authors show that all algorithms find similar Pareto front approximations with good solution quality. Similarly, Vinctan et al [27] deal with design space exploration by implementing a meta-optimization layer for the tool Framework for Automatic Design Space Exploration. With this approach, it is possible to introduce a metaoptimization function that can use multiple meta-heuristics simultaneously by switching between them at simulation runtime.…”

Section: Related Workmentioning

confidence: 99%

“…The data type key defines the data type of the input parameter option, where we accept int and double (Section III-E4, line 25,29). The min and max keys allow the user to specify the value range the input parameter can take (Section III-E4, line 26,27,30,31). Finally, the strategies key allows the user to define for which adaptation planning strategy this input parameter is meaningful by defining a list of strategies (Section III-E4, line 33).…”

Section: Model-based Reasoning Process Model Learning Process Processmentioning

confidence: 99%

“…This idea of combined reasoning and learning can be found in the SeAC research area, whose ideas and approaches will be applied in this work. There are several approaches to situation detection [11], [12], [13], [14], [15], [16], [17], algorithm selection [18], [19], [20], [21], [22], and parameter optimization [23], [24], [25], [26], [27] especially in the SAS literature. However, there is no integrated approach that combines these ideas into a mechanism that is generalizable and applicable to a variety of use cases.…”

Section: Introductionmentioning

confidence: 99%

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A Case Study on Optimization of Platooning Coordination

Hadry¹,

Kounev²,

Krupitzer³

2021

Preprint

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In today's world, circumstances, processes, and requirements for software systems are becoming increasingly complex. In order to operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close intervehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one platooning coordination strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this paper, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.

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Section: Related Workmentioning

confidence: 99%

Section: Model-based Reasoning Process Model Learning Process Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Case Study on Optimization of Platooning Coordination

Hadry¹,

Kounev²,

Krupitzer³

2021

Preprint

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“…In [3], a meta optimization function that uses various metaheuristics simultaneously to obtain better results in appropriate time has been introduced. The difference in our approach and in [3] is the use of round-robin and the random hyper-heuristic introduction in FADSE to optimize the process of DSE. In this work, several meta-heuristic approaches in random and roundrobin have been used to acquire the optimized solution in appropriate time.…”

Section: Imentioning

confidence: 99%

Towards Multi-objective Optimization of Automatic Design Space Exploration for Computer Architecture through Hyper-heuristic

Latif

Ismail

2019

Eng. Technol. Appl. Sci. Res.

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Multi-objective optimization is an NP-hard problem. ADSE (automatic design space exploration) using heuristics has been proved to be an appropriate method in resolving this problem. This paper presents a hyper-heuristic technique to solve the DSE issue in computer architecture. Two algorithms are proposed. A hyper-heuristic layer has been added to the FADSE (framework for automatic design space exploration) and relevant algorithms have been implemented. The benefits of already existing multi-objective algorithms have been joined in order to strengthen the proposed algorithms. The proposed algorithms, namely RRSNS (round-robin scheduling NSGA-II and SPEA2) and RSNS (random scheduling NSGA-II and SPEA2) have been evaluated for the ADSE problem. The results have been compared with NSGA-II and SPEA2 algorithms. Results show that the proposed methodologies give competitive outcomes in comparison with NSGA-II and SPEA2.

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“…The software design allows the implementation of different meta-optimization a rithms, such as that presented in [63]. It would be possible to create at least The main features of PlatEMO are as follows.…”

mentioning

confidence: 99%

A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks

Pătrăușanu,

Florea,

Neghină

et al. 2024

Processes

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The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on.

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Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization

Cited by 11 publications

References 14 publications

A Case Study on Optimization of Platooning Coordination

A Case Study on Optimization of Platooning Coordination

Towards Multi-objective Optimization of Automatic Design Space Exploration for Computer Architecture through Hyper-heuristic

A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks

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