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检索条件"主题词=Optimization Algorithms"

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Many-Objective Evolutionary optimization Algorithm based on the Conflict Measurement Grouping Strategy 4

Many-Objective Evolutionary Optimization Algorithm based on ...

引用

4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Jianzhong Shi, Xuhua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331531225

In order to address the convergence issues faced by traditional multi-objective optimization algorithms as the number of objectives increases, this paper proposes a new optimization algorithm, MaOEA-SPC, based on the conflict measurement grouping strategy. First, due to the inherent correlation between subproblems and many-objective optimization problems, we propose a conflict measurement grouping strategy to facilitate knowledge transfer. Next, we extract the distribution characteristics of Pareto solutions during the evolution process of subproblems to establish a Gaussian distribution model that serves as knowledge information. Subsequently, the new individuals generated by the distribution model are added to the population of MaOPs. Finally, the simulation results indicate that the MaOEA-SPC algorithm effectively enhances the convergence speed of MaOPs without lowering the quality of the solution. © 2024 IEEE.

关键词： optimization algorithms

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Distance-Based Subset Selection With a Knee Point for Multi-Objective optimization 13

Distance-Based Subset Selection With a Knee Point for Multi-...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Pang, Lie Meng Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350373332

Recently, the use of an unbounded external archive in the design of evolutionary multi-objective optimization (EMO) algorithms has received increasing attention. An important component in the use of an unbounded external archive is its subset selection method used to select a pre-specified number of solutions from the archive. One of the most commonly used subset selection method is distance-based subset selection (DSS) since it is very easy to calculate and can easily scale to many-objective optimization. However, the DSS's selection mechanism usually lead to a small hypervolume value for the selected subset even when the optimal subset with respect to the minimum distance among the selected solutions is obtained. In this paper, we propose a modification to the DSS algorithm by selecting a knee point as the first solution. The knee point is often a solution of interest to decision makers in the absence of preference information. Experimental results show that the inclusion of a knee point generally has a positive effect on improving the hypervolume value of the selected subset. © 2024 IEEE.

关键词： optimization algorithms

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Random Exploration in Bayesian optimization: Order-Optimal Regret and Computational Efficiency 41

Random Exploration in Bayesian Optimization: Order-Optimal R...

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41st International Conference on Machine Learning, ICML 2024

作者： Salgia, Sudeep Vakili, Sattar Zhao, Qing Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States MediaTek Research Cambridge United Kingdom Department of Electrical and Computer Engineering Cornell University IthacaNY United States

We consider Bayesian optimization using Gaussian Process models, also referred to as kernel-based bandit optimization. We study the methodology of exploring the domain using random samples drawn from a distribution. We show that this random exploration approach achieves the optimal error rates. Our analysis is based on novel concentration bounds in an infinite dimensional Hilbert space established in this work, which may be of independent interest. We further develop an algorithm based on random exploration with domain shrinking and establish its order-optimal regret guarantees under both noise-free and noisy settings. In the noise-free setting, our analysis closes the existing gap in regret performance under a mild assumption on the underlying function and thereby partially resolves a COLT open problem. The proposed algorithm also enjoys a computational advantage over prevailing methods due to the random exploration that obviates the expensive optimization of a non-convex acquisition function for choosing the query points at each iteration. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Design of nearly linear phase IIR fractional order digital differentiators using Iterative Convex optimization algorithm

Design of nearly linear phase IIR fractional order digital d...

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2024 China Automation Congress, CAC 2024

作者： Chen, Juan Wei, Xuewen Meng, Hailong School of Mathematics and Statistics Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9798350368604

This paper introduces a methodology for the design of IIR fractional-order digital differentiators with near-linear phase properties. The primary objective is to minimize the maximum phase deviation from the ideal linear phase, subject to constraints on stability and relative magnitude response deviations. This design incorporates group delay as an optimization variable, leading to a nonconvex optimization problem. To resolve this, we propose an Iterative Convex optimization algorithm that reformulates the nonconvex problem into a series of convex subproblems utilizing a Gauss-Newton strategy. Simulation experiments have substantiated the superiority of the proposed differentiator compared to methods in the field, demonstrating significantly reduced relative magnitude deviations and maximum phase errors. © 2024 IEEE.

关键词： optimization algorithms

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ma, Wenqiang Hau, Yaqing Yu, Hua Tong, Xiangrong Zhu, Zexuan Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350308365

Deep neural networks are powerful and popular learning models;however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem. © 2024 IEEE.

关键词： optimization algorithms

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Distribution Network Reconfiguration Based on Improved Particle Swarm optimization Algorithm 3

Distribution Network Reconfiguration Based on Improved Parti...

引用

3rd International Conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Li, Tengshuo Ge, Jie Zhu, Ruijin Tibet Agricultural and Animal Husbandry University Bayi Tibet Nyingchi China State Grid Tibet Power Company Ultra High Voltage Branch Tibet Nyingchi China

ISBN: (纸本)9798350352658

In response to the challenges posed by the significant integration of distributed energy resources on distribution network reconfiguration, an improved particle swarm optimization algorithm is proposed in this paper. The optimization algorithm utilizes Sobol sequences to optimize the initial positions of particles, enhancing the quality of initial solutions. Subsequently, through linearly decreasing inertia weights to balance global exploration and local exploitation, combined with an update mechanism adjusted by a compression factor to precisely control the step size, the search efficiency is improved, and the algorithm convergence is accelerated. Additionally, the introduction of an escape mechanism further strengthens global search capabilities, effectively preventing premature convergence of the algorithm. Validation on the IEEE 33-node test system demonstrates the significant advantages of this algorithm in terms of optimization performance, showcasing its potential application in the field of distribution network reconfiguration. © 2024 IEEE.

关键词： optimization algorithms

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IMPLEMENTATION OF NETWORK optimization AND RESILIENCY ANALYSIS TOWARDS MISSION ASSURANCE

IMPLEMENTATION OF NETWORK OPTIMIZATION AND RESILIENCY ANALYS...

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ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2024

作者： Scott, Daniel J. Jensen, David C. University of Arkansas FayettevilleAR United States

ISBN: (纸本)9780791888353

Network optimization and resiliency analysis are pivotal domains revealing network functionality, strength, and resilience. Despite their promise, these methodologies often encounter integration limitations, scalability issues, or functional gaps, prompting the need for further development. This paper explores the characteristics of network optimization and resiliency analysis, presenting existing methodologies, discussing their limitations, and proposing a new approach. Our proposed method integrates genetic algorithms (GAs) and weighted degradation analysis to address these shortcomings effectively. Our research emphasizes the integration of GAs for network optimization and weighted degradation analysis for resiliency assessment. Through a case study focusing on an electrical grid system, we elucidate the efficacy and challenges of our proposed methodologies. While GAs efficiently generate optimized networks, they encounter inconsistencies due to algorithmic issues. Similarly, weighted degradation analysis demonstrates forward propagation capabilities but faces challenges in assessing backpropagation issues adequately. Network optimization and resiliency analysis play a crucial role in understanding network characteristics, contributing to overall network assurance and mission success. This study underscores the importance of ongoing refinement and resolution of algorithmic issues to fully leverage these methodologies. Further research is essential to overcoming existing challenges and seamlessly integrating these methodologies into mission-critical systems, thereby enhancing network understanding and optimization capabilities. Copyright © 2024 by ASME.

关键词： optimization algorithms

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Research on optimization of Byzantine Fault-Tolerant Consensus Algorithm Based on Involvement 3

Research on Optimization of Byzantine Fault-Tolerant Consens...

引用

3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhang, Han Wang, Baocheng Guo, Hao The Northern Industrial University College of Information Shijingshan District Beijing China School of Information Science North China University of Technology Shijingshan District Beijing China

ISBN: (纸本)9798331506582

The league chain is widely used in practical Byzantine fault tolerance consensus algorithm as the low throughput, delay higher problem. In order to solve the above problems, this paper proposes an optimization research of Byzantine fault tolerant consensus algorithm based on involvement, namely IBFT algorithm. During initialization, buffer node area, divided into node in the process of consensus dynamic add delete;As you progress through the consensus process, calculation with the involvement of consensus node number, in order to calculate its involvement;Finally, the nodes were dynamically replaced according to the level of involvement. The simulation results show that compared with the traditional PBFT algorithm, IBFT algorithm in the time delay are lower, in the process of consensus a greater throughput. © 2024 IEEE.

关键词： optimization algorithms

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Enhancing Intrusion Detection Systems: A Comparative Study using Whale optimization Algorithm and Hawks optimization Algorithm 11

Enhancing Intrusion Detection Systems: A Comparative Study u...

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11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Abualhaj, Mosleh M. Al-Khatib, Sumaya N. Al-Allawee, Ali Munther, Alhamza Anbar, Mohammed Al-Ahliyya Amman University Department of Networks and Cybersecurity Amman Jordan Irimas University of Haute Alsace Colmar France University Technology and Applied Sciences It Department Sur Oman Universiti Sains Malaysia Penang Malaysia

ISBN: (纸本)9798350362541

Intrusion Detection Systems (IDS) play a pivotal role in safeguarding computer networks against malicious activities. This research explores the efficacy of two optimization algorithms, namely Whale optimization Algorithm (WOA) and Hawks optimization (HHO), for feature selection to enhance the performance of IDS. The NSL-KDD dataset is utilized as a benchmark to evaluate the impact of selected features on classification accuracy. The study employs the Random Forest (RF) classification algorithm to evaluate the effectiveness of feature selection methods. Implementation is conducted in Python, leveraging its versatile libraries and frameworks. Comparative analysis reveals that when integrating the selected features, RF attains superior results compared to utilizing features separately for binary and multiclass classifications. Results indicate that RF with HHO achieves an impressive accuracy of 98.02%, showcasing the prowess of HHO in enhancing IDS performance. Meanwhile, RF with WOA achieves a commendable accuracy of 97.81%. © 2024 IEEE.

关键词： optimization algorithms

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Pareto Front Estimation Model optimization for Aggregative Solution Set Representation 13

Pareto Front Estimation Model Optimization for Aggregative S...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

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

ISBN: (纸本)9798350308365

This paper aims to enhance the accuracy of the Pareto front estimation model as an aggregative representation of the non-dominated solutions and proposes an algorithm named the Pareto front Model optimization Algorithm (PFMOA). The typical output of multi-objective optimization is a set of non-dominated solutions approximating the Pareto front, which is the optimal trade-off between objective function values. The more non-dominated solutions there are, the more accurately the Pareto front can be approximated. However, especially in real-world problems, there is often a limitation on increasing the number of solutions due to the time required to execute objective functions. For the issue, a Pareto front estimation method interpolates between a limited number of non-dominated solutions to represent changes in objective function values even in regions where non-dominated solutions are not actually obtained. The proposed PFMOA enhances the accuracy of the Pareto front estimation model while evaluating new solutions. PFMOA generates the estimated Pareto front based on a known non-dominated solution set using Kriging. PFMOA focuses on the point with the lowest confidence levels on the estimated Pareto front and evaluates the corresponding point on the estimated Pareto set. PFMOA also generates new solutions using evolutionary variation. PFMOA stochastically switches these two model-based and evolutionary-based solution generation methods. If the new solution is non-dominated, it is included in the known non-dominated solution set, and the process is repeated to enhance the accuracy of the Pareto front estimation model. The effectiveness of the proposed PFMOA is verified using DTLZ1-3, and WFG4 problems. The results show that, in all cases, the accuracy of the Pareto front approximation by the Pareto front estimation model is higher than that by the obtained solution set itself. Additionally, the combination of model-based and evolutionary-based solution generation is bene

关键词： optimization algorithms

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