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28th International Conference on System Theory Control and Computing

作者： Patrausanu, Andrei Florea, Adrian Neghina, Mihai Chis, Radu Dicoiu, Alina Lucian Blaga Univ Sibiu Comp Sci & Elect Engn Dept Sibiu Romania

ISBN: (纸本)9798350364309;9798350364293

The car suspension system has a major impact over the drivers' safety, since the public road might suffer various elevation changes and damage. This work focuses on finding optimal parameters of suspensions models, which are responsible for both safety and comfort of passengers while driving. We considered 4 different suspension architectures: 2-DOF, 3-DOF, 4-DOF quarter car and 5-DOF half car. These optimal configurations have been obtained using relatively recent proposed bio-inspired algorithms such as RDA, IRDA and MOGWO. All the simulations have been performed on different road profiles and different types of cars. Additionally, we propose 6 meta-optimization schemes called super-position (SP1 - SP6), that is to combine algorithms (RDA+MOGWO) in order to enhance the quality of the obtained configurations. The results show that the proposed super-position methods of RDA+MOGWO outperform previous similar methods, such as NSGA-II+SPEA2, by an increase of 3.44 % in quality.

关键词： manufacturing optimization design suspensions cars bio-inspired multi-objective evolutionary algorithms super-position

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Experiences Using Julia for Implementing Multi-objective evolutionary algorithms 1

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15th International Conference of the Metaheuristics International Conference (MIC)

作者： Nebro, Antonio J. Gandibleux, Xavier Univ Malaga Dept Lenguajes & Ciencias Comp Malaga 29071 Spain Univ Malaga ITIS Software Malaga 29071 Spain Nantes Univ Fac Sci & Tech Dept Informat Lab Sci Numer NantesUMR CNRS 6004 Nantes France

ISBN: (数字)9783031629228

ISBN: (纸本)9783031629211;9783031629228

Julia is a programming language suitable for data analysis and scientific computing that combines simplicity of productivity languages with characteristics of performance-oriented languages. In this paper, we are interested in studying the use of Julia to implement Multi-Objective MetaHeuristics. Concretely, we use the Java-based jMetal framework as a reference support and investigate how Julia could be used to design and develop the component-based architecture for multi-objective evolutionary algorithms that jMetal provides. By using the NSGA-II algorithm as an example, we analyze the advantages and short-comings of using Julia in this context, including aspects related to reusing jMetal code and a performance comparison.

关键词： Multi-Objective MetaHeuristics evolutionary algorithms Julia Programming Language Open-Source Solver

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Using evolutionary algorithms to Find Cache-Friendly Generalized Morton Layouts for Arrays 24

Using Evolutionary Algorithms to Find Cache-Friendly General...

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15th ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Swatman, Stephen Nicholas Varbanescu, Ana-Lucia Pimentel, Andy D. Salzburger, Andreas Krasznahorkay, Attila Univ Amsterdam Amsterdam Netherlands Univ Twente Enschede Netherlands CERN Geneva Switzerland

ISBN: (纸本)9798400704444

The layout of multi-dimensional data can have a significant impact on the efficacy of hardware caches and, by extension, the performance of applications. Common multi-dimensional layouts include the canonical row-major and column-major layouts as well as the Morton curve layout. In this paper, we describe how the Morton layout can be generalized to a very large family of multi-dimensional data layouts with widely varying performance characteristics. We posit that this design space can be efficiently explored using a combinatorial evolutionary methodology based on genetic algorithms. To this end, we propose a chromosomal representation for such layouts as well as a methodology for estimating the fitness of array layouts using cache simulation. We show that our fitness function correlates to kernel running time in real hardware, and that our evolutionary strategy allows us to find candidates with favorable simulated cache properties in four out of the eight real-world applications under consideration in a small number of generations. Finally, we demonstrate that the array layouts found using our evolutionary method perform well not only in simulated environments but that they can effect significant performance gains-up to a factor ten in extreme cases-in real hardware.

关键词： Morton curve Z-order curve space-filling curves array layout multi-dimensional data evolutionary algorithms caching locality

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Energy Consumption of evolutionary algorithms in JavaScript 17th

Energy Consumption of Evolutionary Algorithms in JavaScript

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17th Italian Workshop on Artificial Life and evolutionary Computation (WIVACE)

作者： Merelo-Guervos, Juan J. Garcia-Valdez, Mario Castillo, Pedro A. Univ Granada Dept Comp Engn Automat & Robot Granada Spain Natl Technol Inst Mexico Dept Grad Studies Tijuana Mexico

ISBN: (纸本)9783031574290;9783031574306

Green computing is a methodology for saving energy when implementing algorithms. In environments where the runtime is an integral part of the application, it is essential to measure their energy efficiency so that researchers and practitioners have enough choice. In this paper, we will focus on JavaScript runtime environments for evolutionary algorithms;although not the most popular language for scientific computing, it is the most popular language for developers, and it has been used repeatedly to implement all kinds of evolutionary algorithms almost since its inception. In this paper, we will focus on the importance of measuring different versions of the same runtimes, as well as extending the EA operators that will be measured. We also like to remark on the importance of testing the operators in different architectures to have a more precise picture that tips the balance towards one runtime or another.

关键词： Green computing metaheuristics JavaScript energy-aware computing evolutionary algorithms

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Step Size Control in evolutionary algorithms for Neural Architecture Search 16th

Step Size Control in Evolutionary Algorithms for Neural Arch...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Nieber, Christian Mota Dias, Douglas Naredo Garcia, Enrique Ryan, Conor Department of Computer Science and Information Systems University of Limerick Limerick Ireland Department of Computer Science & Applied Physics Atlantic Technological University Galway Ireland Departamento de Ciencias Básicas Universidad del Caribe Cancun Mexico

ISBN: (纸本)9789897587214

This work examines how evolutionary Neural Architecture Search (NAS) algorithms can be improved by controlling the step size of the mutation of numerical parameters. The proposed NAS algorithms are based on F-DENSER, a variation of Dynamic Structured Grammatical Evolution (DSGE). Overall, a (1+5) evolutionary Strategy is used. Two methods of controlling the step size of mutations of numeric values are compared to Random Search and F-DENSER: Decay of the step size over time and adaptive step size for mutations. The search for lightweight, LeNet-like CNN architectures for MNIST classification is used as a benchmark, optimizing for both accuracy and small architectures. An architecture is described by about 30 evolvable parameters. Experiments show that with step size control, convergence is faster, better performing neural architectures are found on average, and with lower variance. The smallest architecture found during the experiments reached an accuracy of 98.8% on MNIST with only 5,450 free parameters, compared to the 62,158 parameters of LeNet-5. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： evolutionary algorithms LeNet Lightweight CNNs MNIST Neural Architecture Search Step Size Control

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I-V-curve analysis using evolutionary algorithms: Hysteresis compensation in fast sun simulator measurements of HJT cells

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SOLAR ENERGY MATERIALS AND SOLAR CELLS 2022年 238卷

作者： Hevisov, David Sporleder, Kai Turek, Marko Fraunhofer Ctr Silicon Photovolta CSP Otto Eissfeldt Str 12 D-06120 Halle An Der Saale Germany

State-of-the-art solar cell technologies, such as hetero-junction cells or PERC cells, exhibit a time-dependent deformation of their current-voltage characteristics in fast solar simulator measurements. This hysteresis effect is due to an increased internal capacitance. It manifests itself as a pronounced difference between I-V-curves depending on the measurement direction, i.e. I-sc -> V-oc or V-oc -> I-sc. Thus, it leads to an imprecise determination of the cell performance parameters in particular at the maximum power point. In this study, an algorithm-based correction procedure for these capacitance-induced effects is presented. Using evolutionary optimization algorithms, our correction approach allows the determination of a steady-state curve together with the extraction of all cell parameters featured in a time-dependent equivalent circuit model. It can be implemented without any hardware upgrades and applied to measurement times as low as a few milliseconds. As our basic approach is entirely independent of the underlying model, it is applicable to any solar cell technology by adapting the model under consideration.

关键词： Solar simulators Fast current-voltage measurement Hysteresis compensation evolutionary algorithms

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Optimization of Reliability–Redundancy Allocation Problems: A Review of the evolutionary algorithms

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Computers, Materials & Continua 2022年第4期71卷 537-571页

作者： Haykel Marouani Omar Al-mutiri College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia University of Monastir LGMENIMAvenue Ibn-Eljazzar5019MonastirTunisia

The study of optimization methods for reliability–redundancy allocation problems is a constantly changing *** algorithms are continually being designed on the basis of observations of nature,wildlife,and *** this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation *** from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability ***,this improvement has been difficult to achieve for high-reliability *** and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.

关键词： Reliability redundancy evolutionary algorithms

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Optimizing the Energy Consumption of Blockchain-Based Systems Using evolutionary algorithms: A New Problem Formulation

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2022年第4期7卷 910-922页

作者： Alofi, Akram Bokhari, Mahmoud A. Bahsoon, Rami Hendley, Robert Univ Birmingham Sch Comp Sci Birmingham B15 2TT England Umm Al Qura Univ Jamoum Univ Coll Comp Sci Dept Jamoum 25372 Saudi Arabia Taibah Univ Comp Sci Dept Software Engn Res Grp Medina 42353 Saudi Arabia Univ Adelaide Sch Comp Sci Optimizat & Logist Grp Adelaide 5005 Australia

Blockchain technology has gained recognition in industrial, financial, and various technological domains for its potential in decentralizing trust in peer-to-peer systems. A core component of blockchain technology is a consensus algorithm, most commonly Proof of Work (PoW). PoW is used in blockchain-based systems to establish trust among peers;however, it does require the expenditure of an enormous amount of energy that affects the environmental sustainability of blockchain-based systems. Energy minimization, whilst ensuring trust within blockchain-based systems that use PoW, is a challenging problem. The solution has to consider how energy consumption can be minimized without compromising trust, whilst still ensuring, for instance, scalability, security, and decentralization. In this paper, we represent the problem as a subset selection problem of miners in a blockchain-based system. We formulate the problem of blockchain energy consumption as a Search-Based Software Engineering problem with four objectives: energy consumption, carbon emission, decentralization, and trust. We propose a model composed of multiple fitness functions. The model can be used to explore the complex search space by selecting a subset of miners that minimizes the energy consumption without drastically impacting the primary goals of the blockchain technology (i.e., security/trustworthiness and decentralization). We integrate our proposed fitness functions into five evolutionary algorithms to solve the problem of blockchain miners selection. Our results show that the environmental sustainability of blockchain-based systems (e.g. reduced energy use) can be enhanced with little degradation in other competing objectives. We also report on the performance of the algorithms used.

关键词： Blockchain evolutionary algorithms mining optimization search-based software engineering sustainability

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CONNECTING LARGE LANGUAGE MODELS WITH evolutionary algorithms YIELDS POWERFUL PROMPT OPTIMIZERS 12

CONNECTING LARGE LANGUAGE MODELS WITH EVOLUTIONARY ALGORITHM...

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12th International Conference on Learning Representations, ICLR 2024

作者： Guo, Qingyan Wang, Rui Guo, Junliang Li, Bei Song, Kaitao Tan, Xu Liu, Guoqing Bian, Jiang Yang, Yujiu Tsinghua University China Microsoft Research United States Northeastern University United States

Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To automate this process, in this paper, we propose a novel framework for discrete prompt optimization, called EVOPROMPT, which borrows the idea of evolutionary algorithms (EAs) as they exhibit good performance and fast convergence. To enable EAs to work on discrete prompts, which are natural language expressions that need to be coherent and human-readable, we connect LLMs with EAs. This approach allows us to simultaneously leverage the powerful language processing capabilities of LLMs and the efficient optimization performance of EAs. Specifically, abstaining from any gradients or parameters, EVOPROMPT starts from a population of prompts and iteratively generates new prompts with LLMs based on the evolutionary operators, improving the population based on the development set. We optimize prompts for both closed- and open-source LLMs including GPT-3.5 and Alpaca, on 31 datasets covering language understanding, generation tasks, as well as BIG-Bench Hard (BBH) tasks. EVOPROMPT significantly outperforms human-engineered prompts and existing methods for automatic prompt generation (e.g., up to 25% on BBH). Furthermore, EVOPROMPT demonstrates that connecting LLMs with EAs creates synergies, which could inspire further research on the combination of LLMs and conventional algorithms. Our code is available at https://***/beeevita/EvoPrompt. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： evolutionary algorithms

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Investigating the Use of Linear Programming and evolutionary algorithms for Multi-Objective Electric Vehicle Charging Problem

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IEEE ACCESS 2022年 10卷 115322-115337页

作者： Singh, Hemant Kumar Ray, Tapabrata Rana, Md Juel Limmer, Steffen Rodemann, Tobias Olhofer, Markus Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Honda Res Inst Europe D-63073 Offenbach Germany

With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of the client EVs over a time horizon while considering multiple objectives, such as operating costs and the peak power drawn from the grid. evolutionary algorithms (EAs) are a popular choice when faced with problems involving multiple objectives. However, since the objectives and constraints of this problem can be expressed using linear functions, it is also possible to come up with improvised multi-objective formulations which can be solved with exact techniques such as mixed-integer linear programming (MILP). With both approaches having their potential strengths and pitfalls, it is worth investigating their use to inform the algorithmic choices, which this study aims to address. In doing so, it makes a number of contributions to the topic, including extension of an existing EV charging problem to a multi-objective form;observing some interesting properties of the problem to improve both the MILP and EA solution approaches;and comparing the performance of MILP and EA. The study provides some useful insights into the problem, initial results and quantitative basis for selecting solution approaches, and highlights some areas of further development.

关键词： Electric vehicle charging Optimization Modulation Mixed integer linear programming evolutionary computation Costs Batteries evolutionary algorithms electric vehicle charging mixed-integer linear programming multi-objective optimization

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