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arXiv 2021年

作者： Zhang, Lijun Wang, Yibo Wang, Guanghui Yi, Jinfeng Yang, Tianbao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China College of Computing Georgia Institute of Technology United States JD AI Research Beijing China Department of Computer Science and Engineering Texas A&M University United States

Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex functions simultaneously. However, they need to design and optimize one surrogate loss for each type of functions, making it difficult to exploit the structure of the problem and utilize existing algorithms. In this paper, we propose a simple strategy for universal online convex optimization, which avoids these limitations. The key idea is to construct a set of experts to process the original online functions, and deploy a meta-algorithm over the linearized losses to aggregate predictions from experts. Specifically, the meta-algorithm is required to yield a second-order bound with excess losses, so that it can leverage strong convexity and exponential concavity to control the meta-regret. In this way, our strategy inherits the theoretical guarantee of any expert designed for strongly convex functions and exponentially concave functions, up to a double logarithmic factor. As a result, we can plug in off-the-shelf online solvers as black-box experts to deliver problem-dependent regret bounds. For general convex functions, it maintains the minimax optimality and also achieves a small-loss bound. Furthermore, we extend our universal strategy to online composite optimization, where the loss function comprises a time-varying function and a fixed regularizer. To deal with the composite loss functions, we employ a meta-algorithm based on the optimistic online learning framework, which not only possesses a second-order bound, but also can utilize estimations for upcoming loss functions. With appropriate configurations, we demonstrate that the additional regularizer does not contribute to the meta-regret, thus maintaining the universality in the composite setting. Copyright © 2021, The Authors. All rights reserved.

关键词： optimization algorithms

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A Landscape-Aware Differential Evolution for Multimodal optimization Problems

arXiv

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arXiv 2024年

作者： Lin, Guo-Yun Chen, Zong-Gan Liu, Chuanbin Jiang, Yuncheng Kwong, Sam Zhang, Jun Zhan, Zhi-Hui School of Computer Science South China Normal University Guangzhou510631 China The Center for Scientific Research and Development in Higher Education Institutes Ministry of Education China The Department of Computing and Decision Science Lingnan University Hong Kong The College of Artificial Intelligence Nankai University Tianjin300350 China The Hanyang University ERICA 15588 Korea Republic of

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual locating a found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or a found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution of the found peaks, which helps explore more peaks. The experiments are conducted on 20 widely-used benchmark MMOPs. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performed algorithms proposed recently and four winner algorithms in the IEEE CEC competitions for multimodal optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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An Intelligent Cognitive System Design for Mobile Robot based on optimization Algorithm

An Intelligent Cognitive System Design for Mobile Robot base...

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Scientific Conference of Electrical Engineering (SCEE)

作者： Ahmed S. Al-Araji Khulood E. Dagher Bakir A. Ibraheem Department of Computer Engineering University of Technology – Iraq Baghdad Iraq Al-Khwarizmi College of Engineering University of Baghdad Baghdad Iraq Department of Control and Systems Eng. University of Technology – Iraq Baghdad Iraq

ISBN: (纸本)9781728115887;9781728115870

In this paper, a cognitive system based on a nonlinear neural controller and an intelligent algorithm is used to guide an autonomous mobile robot during continuous path-tracking. The controller will navigate over solid obstacles with avoidance. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment in order to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm optimization and Artificial Bee Colony algorithms are used for finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths. The algorithms are used for finding the reference path equation of the optimal path, as well as finding and tuning the neural control gains values online for the nonlinear neural controller. The goal is to obtain the best torques actions of the wheels for the mining autonomous mobile robot. Simulation results are done by using MATLAB which showed that the proposed cognitive system is more accurate in terms of planning a reference path to avoid obstacles, online finding, and tuning parameters of the controller which generates smooth control actions without saturation for tracking the reference path equation, as well as minimizing the mobile robot tracking pose error.

关键词： collision avoidance mobile robots neurocontrollers particle swarm optimisation Mobile robots Neurocontrollers Particle swarm optimization Collision avoidance autonomous mobile robot optimization algorithms path equation Controllers Mine Environment FINDINGS control action Mobile robot navigation optimal path

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The "Black-Box" optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation

arXiv

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arXiv 2023年

作者： Lobanov, Aleksandr Bashirov, Nail Gasnikov, Alexander Moscow Institute of Physics and Technology 9 Institutskiy per. Dolgoprudny141701 Russia Skolkovo Institute of Science and Technology 30 Bolshoy Boulevard bld. 1 Moscow121205 Russia ISP RAS Research Center for Trusted Artificial Intelligence 25 A. Solzhenitsyn st. Moscow125047 Russia Institute for Information Transmission Problems 19 B. Karetny per. Moscow127051 Russia Innopolis University 1 Universitetskaya Str. Innopolis420500 Russia

In this paper, we study the standard formulation of an optimization problem when the computation of gradient is not available. Such a problem can be classified as a "black box" optimization problem, since the oracle returns only the value of the objective function at the requested point, possibly with some stochastic noise. Assuming convex, and higher-order of smoothness of the objective function, this paper provides a zero-order accelerated stochastic gradient descent (ZO-AccSGD) method for solving this problem, which exploits the higher-order of smoothness information via kernel approximation. As theoretical results, we show that the ZO-AccSGD algorithm proposed in this paper improves the convergence results of state-of-the-art (SOTA) algorithms, namely the estimate of iteration complexity. In addition, our theoretical analysis provides an estimate of the maximum allowable noise level at which the desired accuracy can be achieved. Validation of our theoretical results is demonstrated both on the model function and on functions of interest in the field of machine learning. We also provide a discussion in which we explain the results obtained and the superiority of the proposed algorithm over SOTA algorithms for solving the original problem. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Zeroth-order Random Subspace Algorithm for Non-smooth Convex optimization

arXiv

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arXiv 2024年

作者： Nozawa, Ryota Poirion, Pierre-Louis Takeda, Akiko Department of Mathematical Informatics The University of Tokyo Tokyo Japan Center for Advanced Intelligence Project RIKEN Tokyo Japan

Zeroth-order optimization, which does not use derivative information, is one of the significant research areas in the field of mathematical optimization and machine learning. Although various studies have explored zeroth-order algorithms, one of the theoretical limitations is that oracle complexity depends on the dimension, i.e., on the number of variables, of the optimization problem. In this paper, to reduce the dependency of the dimension in oracle complexity, we propose a zeroth-order random subspace algorithm by combining a gradient-free algorithm (specifically, Gaussian randomized smoothing with central differences) with random projection. We derive the worst-case oracle complexity of our proposed method in non-smooth and convex settings;it is equivalent to standard results for full-dimensional non-smooth convex algorithms. Furthermore, we prove that ours also has a local convergence rate independent of the original dimension under additional assumptions. In addition to the theoretical results, numerical experiments show that when an objective function has a specific structure, the proposed method can become experimentally more efficient due to random *** Codes 90C56 (Primary) 49J52, 60B20, 90C06, 90C25 (Secondary) © 2024, CC BY.

关键词： optimization algorithms

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A routing cooperative selection method based on ant colony optimization algorithm

A routing cooperative selection method based on ant colony o...

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International Conference on Cyberspace Technology (CCT 2014)

作者： Sun Dandan Zhang Hongxin School of Electronic Engineering Beijing University of Posts and Telecommunications 100876 China Sch. of Electron. Eng. Beijing Univ. of Posts & Telecommun. Beijing China

ISBN: (纸本)9781510802704

Cooperative routing selection algorithm is based on the limited bandwidth and collaborative remain energy, according to pheromone concentration in Ant colony optimization algorithm. The ant colony optimization algorithm is applied to improving collaboration network routing problems to reduce network congestion and reduce node energy consumption. Simulation demonstrate that the algorithm obtains a global optimization and makes an average 60% reduction in end to end delay, increased the utilization of network resources.

关键词： Ant colony optimization algorithm Pheromone density The Cooperative routing ant algorithm Ant colony optimization Network routing Web resources optimization algorithms Network Congestion selection algorithm Global optimization algorithms selection methods node energy

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Universal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization

arXiv

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arXiv 2024年

作者： Guigues, Vincent Liang, Jiaming Monteiro, Renato D.C. School of Applied Mathematics FGV/EMAp Rio de Janeiro22250-900 Brazil Goergen Institute for Data Science Department of Computer Science University of Rochester RochesterNY14620 United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bounds for the two methods are established in terms of the unknown strong convexity parameter. To the best of our knowledge, the two proposed methods are the first universal methods for solving hybrid strongly convex composite optimization problems that do not rely on any restart scheme nor require the knowledge of the optimal value. © 2024, CC BY.

关键词： optimization algorithms

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Hybrid Inference optimization for Robust Pose Graph Estimation

Hybrid Inference Optimization for Robust Pose Graph Estimati...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Aleksandr V. Segal Ian D. Reid University of Oxford University of Adelaide

ISBN: (纸本)9781479969357

In this paper we introduce a new optimization algorithm for networks of switched nonlinear objectives and apply this to the important problem of pose graph estimation for robot localization and mapping. The key insight is to replace the linear solver typically used in Gauss-Newton style methods with hybrid inference over switched discrete/continuous linear Gaussian networks. Since exact inference in these networks is known to be NP-hard, we also propose an approximate inference algorithm for the linearized hybrid networks based on message passing. We apply the new algorithm to the problem of robust pose graph estimation in the presence of incorrect loop closures and compare against three recently published approaches to the same problem. Evaluation is performed on ten sequences from two different datasets and shows that our approach performs substantially better than the state of the art.

关键词： optimization algorithms state of the art hybrid network Inference algorithms Robustness Network Nondeterministic polynomial-time hard

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A Nested Evolutionary Algorithm for Solving a Bilevel Competitive Location Problem: Optimistic vs. Pessimistic Approaches 13

A Nested Evolutionary Algorithm for Solving a Bilevel Compet...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Camacho-Vallejo, José-Fernando Corpus, Carlos School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Universidad Autónoma de Nuevo León Facultad de Ciencias Físico-Matemáticas San Nicolás de los Garza Mexico

ISBN: (纸本)9798350308365

Competitive facility location problems involve two different firms competing for customers, each aiming to increase its market share. Typically, there exists a hierarchy between the firms based on factors such as size, power, or influence in the market. In this type of problem, it is commonly assumed that both firms will open a fixed number of facilities. Moreover, the firm acting as the leader in the market must consider the reaction of the other firm, considered as the follower. The follower's decisions are conditioned by the leader's decisions, as the facilities opened by the leader directly affect the follower's choices. Both firms aim to maximize their own market share. Additionally, in this study, it is assumed that the follower is interested in locating its facilities as far apart from each other as possible. Therefore, a bilevel optimization problem is formulated, where the leader considers a mono-objective problem, and the follower considers a bi-objective problem. It is important to note that obtaining the follower's reaction introduces some ambiguity since the follower's response is given as a Pareto front. An assumption must be made regarding this crucial issue. In this study, two well-known approaches are considered: the optimistic and the pessimistic ones. To solve the problem, two versions of an evolutionary algorithm embedded with an ϵ -constraint method and with an NSGA-II are proposed. Hence, two variants of the algorithm are implemented, one for each approach assumed. Computational experimentation is conducted to validate the performance of the proposed algorithms in solving this complex problem. Moreover, a comparison regarding the impact caused by assuming the optimistic or the pessimistic approaches is performed. The obtained results reveal relevant findings, and an interesting direction for further research is identified. © 2024 IEEE.

关键词： optimization algorithms

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DMEA-II and its application on spam email detection problems

DMEA-II and its application on spam email detection problems

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7th IEEE Symposium on Computational Intelligence for Security and Defense Applications (IEEE-CISDA)

作者： Long Nguyen Anh Quang Tran Lam Thu Bui Le Quy Don Tech Univ Fac Informat Technol Hanoi Vietnam Univ Hanoi Fac Informat Technol Hanoi Vietnam

ISBN: (纸本)9781479954315

This paper deals with multi-objectitivty in the problem of Vietnamese spam detection. We first analyze the problem taking into account the specific Vietnamese characterises as well as multi-objectivity. With the use of multi-objectivity, we can allow the users more flexibility on selecting the solution. Our proposal is to extende a multi-objective optimization algorithm using directional information, called DMEA-II for finding sets of feasible trade-off solutions for an anti-spam email system (using Apache SpamAssassin). The two objectives for considering are the Spam Detection Rate (SDR) and False Alarm Rate (FAR). The experiments were conducted based on spam data sets through several scenarios with different numbers of SpamAssassin rules. According to the obtained results, the new approach based on DMEA-II not only achieved more efficient results but also created a set of ready-to-use rule scores trading-off between SDR and FAR. It demonstrates the ability to give users more flexibility and efficiency in the Anti-spam email System.

关键词： optimisation security of data unsolicited e-mail Apache Spam Assassin DMEA-II FAR SDR Vietnamese spam detection antispam email system directional information false alarm rate multiobjective optimization algorithm spam detection rate spam email detection problems Databases Electronic mail Optical fibers optimization Sociology Statistics Training Unsolicited electronic mail FEDERAL AVIATION REGULATION SDPR gene false alarm probability electronic mail Data Security Sociology Optical Fibers optimization algorithms Database Functional training

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