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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Benford, Alistair Olhofer, Markus Rodemann, Tobias Lehre, Per Kristian School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom Honda Research Institute Europe Carl-Legien-Straße 30 Offenbach/Main63073 Germany

ISBN: (纸本)9798350308365

A common aim in real-world optimisation problems is to seek a solution offering highest performance on expected scenarios, but at the same time guaranteeing an at least acceptable performance on worst-case scenarios. Competitive coevolution evolves a population of solutions alongside a population of difficult scenarios in order to find so-called robust solutions. However, solutions with maximal worst-case performance often exhibit poor performance on more typical scenarios. Existing coevolutionary approaches generally favour such solutions over ones which sacrifice only a small amount of average performance for an almost as large gain in worst-case performance, despite the latter being favourable in most practical applications. We present a new coevolutionary algorithm which treats average performance and worst-case performance as two objectives of a bicriteria optimisation problem and seeks the corresponding Pareto front. Such an algorithm enables the discovery of solutions with strong performance in both of these metrics, which would otherwise be rejected if optimising for only one. Our algorithm constitutes the first coevolutionary approach to this solution concept. We also provide experimental results on the performance of this algorithm on the design of smart controllers for the management of energy flow between buildings, renewable energy sources, and electric vehicles. © 2024 IEEE.

关键词： optimization algorithms

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Enhancing Energy Efficiency in Cloud Data Centers 15

Enhancing Energy Efficiency in Cloud Data Centers

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Priya, N. Aruna, P. Danusri, E. Ramalakshmi, S. Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798331300579

Cloud data centers play a vital role in meeting the growing demand for cloud services, yet their operations are accompanied by significant energy consumption and environmental impact. Addressing this challenge requires effective optimization strategies tailored to the dynamic nature of cloud workloads and the complex interactions within data center infrastructures. This paper investigates the potential of the Particle Swarm optimization (PSO) algorithm to enhance energy efficiency in cloud data centers. Unlike traditional optimization methods, PSO offers advantages such as the ability to handle multiple objectives simultaneously, adaptability to dynamic environments, and scalability to large-scale data center infrastructures. By leveraging these capabilities, PSO holds promise for optimizing energy usage while maintaining satisfactory performance levels in cloud data centers. This study delves into the methodology of applying PSO to energy optimization in cloud environments, including algorithmic details, adaptation strategies, and implementation considerations. Empirical analysis and experimentation demonstrate the effectiveness of PSO in improving energy efficiency, highlighting its potential as a valuable tool for mitigating energy consumption and environmental impact in cloud data center operations. © Grenze Scientific Society, 2024.

关键词： Cloud data centers dynamic workloads energy consumption energy efficiency environmental impact infrastructure management optimization algorithms Particle Swarm optimization (PSO) performance optimization sustainability

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Hybrid strategy improved Subtraction-Average-Based Optimizer 24

Hybrid strategy improved Subtraction-Average-Based Optimizer

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4th International Conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Li, Longbin Cai, Maoguo Xie, Yukun Li, Bin College of Electronic and Information Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400710247

A hybrid strategy, the improved Subtraction Average Optimizer (HSABO), is proposed to address the shortcomings of the Subtraction Average Based Optimizer (SABO) in handling complex problems such as poor convergence accuracy and susceptibility to local optimal. Firstly, Cubic mapping is applied for population initialization, thereby improving the overall search potential of the optimization algorithm. Secondly, adaptive T-distribution disturbance is introduced in the loop process. Finally, the golden sine strategy is used to correct the global optimum and population. Eight test functions with single-peak and multi-peak characteristics were used for comparison simulation experiments of various algorithms and improved ablation experiments of three strategies. Findings demonstrate that the three refined strategies effectively improve the performance of the conventional SABO algorithm, and the HSABO algorithm excels in performance compared to other algorithms. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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A Comparative Analysis of Genetic algorithms and NSGA-II on the Portfolio Optimisation Problem 26

A Comparative Analysis of Genetic Algorithms and NSGA-II on ...

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26th International Symposium on Symbolic and Numeric algorithms for Scientific Computing, SYNASC 2024

作者： Ilade, Tudor-Florentin Croitoru, Eugen Alexandru Ioan Cuza University Department of Computer Science Iasi Romania

ISBN: (纸本)9798331532833

This paper contains a comparison between a Genetic Algorithm (GA) and a Non-dominated Sorting Genetic Algorithm II (NSGA-II) on the Portfolio Optimisation Problem, based on the Modern Portfolio Theory proposed by Markowitz (1952, 1956). We use the real-world data of the S&P 500 index (quarterly returns of top 200 stocks from 2019 to 2024, and top 442 stocks from 2004 to 2016), and show that both algorithms can yield returns above the index, in mixed bull and bear market conditions. N-point Crossover accelerates algorithm convergence, and by using the Sharpe Ratio, the NSGA-II outperformed most models based on Stochastic Dominance we tested, according to the metrics in Bruni el. al. (2017). © 2024 IEEE.

关键词： optimization algorithms

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Evolutionary Intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

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Computers, Materials & Continua 2022年第10期73卷 87-101页

作者： Bassam A.Y.Alqaralleh Fahad Aldhaban Anas Abukaraki Esam A.Al.Qaralleh MIS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal UniversityMa’an71111Jordan School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan

Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the *** fundus images are generally used by physicians to detect and classify the stages of *** manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial Intelligence(AI)to diagnose the disease have become *** this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)*** intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus *** addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected ***,the fusion of two DL models namely,CapsNet and MobileNet is used for feature ***,the hyperparameter tuning of these models is also performed via Coyote optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify *** experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.

关键词： optimization algorithms medical images diabetic retinopathy deep learning fusion model

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Optimizing sensitivity parameters of automated driving vehicles in an open heterogeneous traffic flow system

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TRANSPORTMETRICA A-TRANSPORT SCIENCE 2022年第3期18卷 762-806页

作者： Bouadi, Marouane Jia, Bin Jiang, Rui Li, Xingang Gao, Ziyou Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol C Minist Transport Beijing 100044 Peoples R China

In this paper, we attempt to address the issue of controlling the sensitivity parameters (or control gains) of automated driving vehicles in an open heterogeneous traffic flow system. The automated driving vehicles are supposedly equipped with adaptive cruise control and connectivity while the conventional vehicles are characterized by a stochastic safe time headway. To optimize the sensitivity parameters, the natural policy gradient reinforcement learning algorithm has been used for the best policy search. In this context, two performance indices were considered: the traffic breakdown probability and fuel consumption. After extensive simulations, it is found that the sensitivity parameters should depend on both the flow and the penetration rate for maximum performance. In particular, a low-penetration rate of 5% can improve traffic performance. A comparison with other algorithms suggests that natural policy gradient and Q-learning yield a good approximation and reduce significantly the computational cost.

关键词： Automated driving vehicles conventional vehicles heterogeneity sensitivity parameters reinforcement learning optimization algorithms

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Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence-based algorithms

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GEOMECHANICS AND ENGINEERING 2022年第1期31卷 113-127页

作者： Zhu, Weixiang Huang, Lihua Mao, Liudan Esmaeili-Falak, Mahzad Zhejiang Guangsha Vocat & Tech Univ Construct Sch Civil Engn Architecture Dongyang 322100 Peoples R China Zhejiang Guangsha Vocat & Tech Univ Construct Sch Management Engn Dongyang Peoples R China Zhejiang Zhongyu Engn Technol Co Ltd Hangzhou Peoples R China Islamic Azad Univ Dept Civil Engn North Tehran Branch Tehran Iran

Because natural coarse aggregates were depleting rapidly, concrete industry has been trended toward substitute aggregates from industrial by-products or waste. One of the waste materials is oil palm ash (OPS), which is widely generated in the processing of palm oil in the tropics. Concretes made with OPS to estimate the compressive strength (CS) is cost and time consuming. This study aims to propose novel hybrid models by concepts of extreme gradient boosting (XGB) model optimized with different optimization algorithms such as sine-cosine algorithm, multiverse optimization algorithm (MVO), and particle swarm optimization for predicting the uniaxial CS (UCS) of oil palm shell lightweight aggregate concrete (OPS). Also, the multivariate adaptive regression spline model is also developed to present a meaningful relationship between input and output variables. To this aim, a data set containing data samples for concrete made with OPS was gathered from the published literature. Results show that all models have acceptable performance in predicting the UCS, representing the admissible correlation between observed and predicted values and models' robustness. In the training step, the value of R2, the root mean square error, and the variance accounted factor for MVO-XGB are 0.9713, 1.5777, and 97.129. These values in testing phase are 0.9019, 2.6786, and 89.158. Therefore, the MVO-XGB model outperforms others, and the results demonstrate the ability of the MVO algorithm to determine the optimal value of XGB parameters.

关键词： extreme gradient boosting lightweight aggregate concrete multivariate adaptive regression spline oil palm shell optimization algorithms uniaxial compressive strength

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A GPU-based Method for Finding Optimal Solution to the Set Covering Problem 31

A GPU-based Method for Finding Optimal Solution to the Set C...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Biswas, Girish Mukherjee, Nandini Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9798331509118

The computation time of the exact and optimal solution to the set covering problem increases exponentially concerning the problem size due to an exhaustive search for the solution. We propose a novel GPU-based parallel method that is successful enough to accelerate the computation of the optimal solution via the exact exhaustive algorithm. This method shows significant SpeedUp in the computation of the optimal solution of the set covering problem with respect to the sequential method. © 2024 IEEE

关键词： optimization algorithms

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Towards an Improved Understanding of Features for More Interpretable Landscape Analysis

Towards an Improved Understanding of Features for More Inter...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Gallagher, Marcus Munoz, Mario University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia The University of Melbourne School of Computer and Information Systems ParkvilleVIC Australia

ISBN: (纸本)9798400704956

Landscape analysis has received increasing attention in the literature, with a major focus on producing feature data to be used in a machine learning pipeline for automatic algorithm selection and configuration. In contrast, the original purpose of landscape analysis (i.e. to develop theory and techniques to understand the structure of fitness landscapes) has been somewhat overshadowed. This paper aims to clarify the purpose of landscape features, emphasizing the need for clear definitions of distance or similarity between optimization problem instances. It argues that current methodologies' shortcomings can be better understood from this perspective and advocates for more interpretable features and distances to gain explainable insights into problem instances, algorithm-problem mappings, and improved benchmarking of black-box algorithms. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Should Multi-objective Evolutionary algorithms Use Always Best Non-dominated Solutions as Parents? 13

Should Multi-objective Evolutionary Algorithms Use Always Be...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Sato, Kazuma Okumura, Naru Takadama, Keiki Sato, Hiroyuki University of Electro-Communications 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350308365

The generational model maintaining both parent and offspring populations is frequently employed for designing multi-objective evolutionary algorithms. The archive population of non-dominated solutions acquired during the search is also often incorporated, especially for real-world problems that cannot lose any valuable information obtained. Although both the parent and archive populations should maintain good solutions, repeated generations may cause solutions in the parent population to be dominated by ones in the archive. This means that the parent population at the final generation cannot be the best solution set throughout the search. This is not problematic if the parent population, with lower optimality than the archive, is beneficial for offspring generation. However, if solutions with the highest optimality are useful for offspring generation, the archive should be used for it. This paper aims to investigate whether the parent population should always be the best non-dominated solution set. As a research methodology, this paper picks NSGA-II, NSGA-III, IBEA, NSGA-II-SDR, and θ-DEA, employing the generational model and re-selecting the parent population from the archive in every certain generation to make the parent population the best. The generational interval of the re-selection is a parameter that adjusts the optimality of the parent population. The shorter the interval, the higher the optimality in the parent population. Experimental results using the knapsack problems with 2-6 objectives show that the highest parent optimality with the shortest re-selection interval maximizes the search performance for many algorithms and problems, while the highest parent optimality does not necessarily maximize the search performance for some algorithms and problems. © 2024 IEEE.

关键词： optimization algorithms

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