Готові списки джерел за темами / Escaping local minima

Добірка наукової літератури з теми "Escaping local minima"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 19 лютого 2023

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Статті в журналах з теми "Escaping local minima"

Damgaard, Malte Rørmose, Rasmus Pedersen, and Thomas Bak. "Escaping Local Minima via Appraisal Driven Responses." Robotics 11, no. 6 (December 16, 2022): 153. http://dx.doi.org/10.3390/robotics11060153.

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Анотація:

Inspired by the reflective and deliberative control mechanisms used in cognitive architectures such as SOAR and Sigma, we propose an alternative decision mechanism driven by architectural appraisals allowing robots to overcome impasses. The presented work builds on and improves on our previous work on a generally applicable decision mechanism with roots in the Standard Model of the Mind and the Generalized Cognitive Hour-glass Model. The proposed decision mechanism provides automatic context-dependent switching between exploration-oriented, goal-oriented, and backtracking behavior, allowing a robot to overcome impasses. A simulation study of two applications utilizing the proposed decision mechanism is presented demonstrating the applicability of the proposed decision mechanism.

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Nasuha, A., A. S. Priambodo, and G. N. P. Pratama. "Vortex Artificial Potential Field for Mobile Robot Path Planning." Journal of Physics: Conference Series 2406, no. 1 (December 1, 2022): 012001. http://dx.doi.org/10.1088/1742-6596/2406/1/012001.

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Abstract Artificial Potential Field (APF) is one of path planning strategies which offers a relatively low cost on computational. It has been implemented in many real-time applications. Unfortunately, it is quite susceptible to local minima problems. In this paper, we offer a scheme for escaping local minima by using vortex field. Based on the results and simulation, it can be verified that the vortex field prevails to navigate the path while escaping local minima.

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Abdi, Mohammed Isam Ismael, Muhammad Umer Khan, Ahmet Güneş, and Deepti Mishra. "Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm." Applied Sciences 10, no. 21 (November 7, 2020): 7905. http://dx.doi.org/10.3390/app10217905.

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The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.

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Koike, Daiki, and Kenji Uchiyama. "Escaping Local Minima Using Repulsive Particles in FastSLAM for Space Rover." International Journal of Mechanical Engineering and Robotics Research 6, no. 6 (2017): 506–11. http://dx.doi.org/10.18178/ijmerr.6.6.506-511.

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Tang, Zheng, Xu Gang Wang, Hiroki Tamura, and Masahiro Ishii. "An Algorithm of Supervised Learning for Multilayer Neural Networks." Neural Computation 15, no. 5 (May 1, 2003): 1125–42. http://dx.doi.org/10.1162/089976603765202686.

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Анотація:

A method of supervised learning for multilayer artificial neural networks to escape local minima is proposed. The learning model has two phases: a backpropagation phase and a gradient ascent phase. The backpropagation phase performs steepest descent on a surface in weight space whose height at any point in weight space is equal to an error measure, and it finds a set of weights minimizing this error measure. When the backpropagation gets stuck in local minima, the gradient ascent phase attempts to fill up the valley by modifying gain parameters in a gradient ascent direction of the error measure. The two phases are repeated until the network gets out of local minima. The algorithm has been tested on benchmark problems, such as exclusive-or (XOR), parity, alphabetic characters learning, Arabic numerals with a noise recognition problem, and a realistic real-world problem: classification of radar returns from the ionosphere. For all of these problems, the systems are shown to be capable of escaping from the backpropagation local minima and converge faster when using the new proposed method than using the simulated annealing techniques.

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ZHANG, WENDONG, and YANPING BAI. "A HYBRID ELASTIC NET METHOD FOR SOLVING THE TRAVELING SALESMAN PROBLEM." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (April 2005): 447–53. http://dx.doi.org/10.1142/s0218194005002233.

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The purpose of this paper is to present a new hybrid Elastic Net (EN) algorithm, by integrating the ideas of the Self Organization Map (SOM) and the strategy of the gradient ascent into the EN algorithm. The new hybrid algorithm has two phases: an EN phase based on SOM and a gradient ascent phase. We acquired the EN phase based on SOM by analyzing the weight between a city and its converging and non-converging nodes at the limit when the EN algorithm produces a tour. Once the EN phase based on SOM stuck in local minima, the gradient ascent algorithm attempts to fill up the valley by modifying parameters in a gradient ascent direction of the energy function. These two phases are repeated until the EN gets out of local minima and produces the short or better tour through cities. We test the algorithm on a set of TSP. For all instances, the algorithm is showed to be capable of escaping from the EN local minima and producing more meaningful tour than the EN.

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Weerakoon, Tharindu, Kazuo Ishii, and Amir Ali Forough Nassiraei. "An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock." Journal of Artificial Intelligence and Soft Computing Research 5, no. 3 (July 1, 2015): 189–203. http://dx.doi.org/10.1515/jaiscr-2015-0028.

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Abstract Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.

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Yi, Junyan, Gang Yang, Xiaoxuan Ma, and Xiaoyun Shen. "An Adaptive Elastic Net Method for Edge Linking of Images." International Journal of Interdisciplinary Telecommunications and Networking 7, no. 2 (April 2015): 7–19. http://dx.doi.org/10.4018/ijitn.2015040101.

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In this paper, the authors propose an adaptive Elastic Net method for edge linking of images. Edge linking is a fundamental computer-vision task, which is a constrained optimization problem. In the proposed method, an adaptive dynamic parameter strategy and a stochastic noise strategy are introduced into the Elastic Net, which enables the network to have superior ability for escaping from local minima and converge sooner to optimal or near-optimal solutions. Simulations confirm that the proposed method could produce more meaningful contours than the original Elastic Net in shorter time.

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Yuan, Mingxin, Yafeng Jiang, Xiaobin Hua, Binbin Wang, and Yi Shen. "A real-time immune planning algorithm incorporating a specific immune mechanism for multi-robots in complex environments." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 231, no. 1 (January 2017): 29–42. http://dx.doi.org/10.1177/0959651816677198.

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Анотація:

To solve the real-time path planning of multi-robots in complex environments, a new immune planning algorithm incorporating a specific immune mechanism is presented. In the immune planning algorithm incorporating a specific immune mechanism, a new coding format for an antibody is first defined according to the impact of the obstacle distribution on the obstacle avoidance behaviors of multi-robots. Then, a new robot immune dynamic model for antibody selection is designed in terms of different impacts of obstacles and targets on robot behaviors. Finally, aiming at the local minimum problem in complex environments and inspired by the specific immune mechanism, a series of appropriate avoidance behaviors are selected through the calculation of a specific immune mechanism to help robots walk out of local minima. In addition, to solve deadlock situations, a learning strategy for the antibody concentration is presented. Compared with four related immune planning algorithms—an improved artificial potential field, a rapidly exploring random tree algorithm, a D* algorithm and a A* algorithm—the simulation results in four static environments show that the paths planned by immune planning algorithm incorporating a specific immune mechanism are the shortest and the path smoothness is generally the highest, which shows its strong planning capability in multi-obstacle environments. The simulation result in a dynamic environment with local minima shows that the immune planning algorithm incorporating a specific immune mechanism has strong planning ability in dynamic obstacle avoidance and in escaping from local minima. Additionally, an experiment in a multi-robot environment shows that two robots can not only avoid static obstacles but also avoid dynamic obstacles, which further supports the validity of the proposed immune planning algorithm incorporating a specific immune mechanism for multi-robots in real environments.

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Yin, Su, and Jonathan Cagan. "An Extended Pattern Search Algorithm for Three-Dimensional Component Layout." Journal of Mechanical Design 122, no. 1 (January 1, 2000): 102–8. http://dx.doi.org/10.1115/1.533550.

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Анотація:

An extended pattern search algorithm is introduced for efficient component layout optimization. The algorithm is applicable to general layout problems, where component geometry can be arbitrary, design goals can be multiple and spatial constraint satisfactions can be of different types. Extensions to pattern search are introduced to help the algorithm to converge to optimal solutions by escaping inferior local minima. The performance on all of the test problems shows that the algorithm runs one-to-two orders of magnitude faster than a robust simulated annealing-based algorithm for results with the same quality. The algorithm is further extended to solve a concurrent layout and routing problem, which demonstrates the ability of the algorithm to apply new pattern strategies in search and to include different objective functions in optimization. [S1050-0472(00)01901-2]

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Більше джерел

Частини книг з теми "Escaping local minima"

Yi, Junyan, Gang Yang, Xiaoxuan Ma, and Xiaoyun Shen. "An Adaptive Elastic Net Method for Edge Linking of Images." In Civil and Environmental Engineering, 921–30. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9619-8.ch039.

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In this chapterr, the authors propose an adaptive Elastic Net method for edge linking of images. Edge linking is a fundamental computer-vision task, which is a constrained optimization problem. In the proposed method, an adaptive dynamic parameter strategy and a stochastic noise strategy are introduced into the Elastic Net, which enables the network to have superior ability for escaping from local minima and converge sooner to optimal or near-optimal solutions. Simulations confirm that the proposed method could produce more meaningful contours than the original Elastic Net in shorter time.

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Jiang, Lihua, and Mingcong Deng. "Support Vector Machine Based Mobile Robot Motion Control and Obstacle Avoidance." In Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance, 223–51. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2086-5.ch008.

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Considering the noise effect during the navigation of a two wheeled mobile robot, SVM and LS-SVM based control schemes are discussed under the measured information with uncertainty, and in the different environments. The noise effect is defined as uncertainty in the measured data. One of them focuses on using a potential function and constructing a plane surface for avoiding the local minima in the static environments, where the controller is based on Lyapunov function candidate. Another one addresses to use a potential function and to define a new detouring virtual force for escaping from the local minima in the dynamic environments. Stability of the control system can be guaranteed. However, the motion control of the mobile robot would be affected by the noise effect. The SVM and LS-SVM for function estimation are used for estimating the parameter in the proposed controllers. With the estimated parameter, the noise effect during the navigation of the mobile robot can be reduced.

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Yaghini, Masoud, and Nasim Gereilinia. "GeneticTKM." In Transportation Systems and Engineering, 651–61. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch033.

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The clustering problem under the criterion of minimum sum square of errors is a non-convex and non-linear problem, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal solution. In this paper, a hybrid genetic, tabu search and k-means algorithm, called GeneticTKM, is proposed for the clustering problem. A new mutation operator is presented based on tabu search algorithm for the proposed hybrid genetic method. The key idea of the new operator is to produce tabu space for escaping from trap of local optimal and finding better solution. The results of the proposed algorithm are compared with other clustering algorithms such as genetic algorithm; tabu search and particle swarm optimization by implementing them and using standard and simulated data sets. The authors also compare the results of the proposed algorithm with other researchers' results in clustering the standard data sets. The results show that the proposed algorithm can be considered as an effective and efficient algorithm to find better solution for the clustering problem.

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Тези доповідей конференцій з теми "Escaping local minima"

Koike, Daiki, and Kenji Uchiyama. "Repulsive Particle Method Considering Escaping Local Minima in Unknown Environment." In International Conference of Control, Dynamic Systems, and Robotics. Avestia Publishing, 2017. http://dx.doi.org/10.11159/cdsr17.132.

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Du, Wei, Sung-Kyun Kim, Oren Salzman, and Maxim Likhachev. "Escaping Local Minima in Search-Based Planning using Soft Duplicate Detection." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8967815.

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Iversen, Thomas Fridolin, and Lars-Peter Ellekilde. "Optimization for Solving Workcell Layouts using Gaussian Penalties for Escaping Local Minima." In 14th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006438201100121.

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Cohen, Eldan, and J. Christopher Beck. "Local Minima, Heavy Tails, and Search Effort for GBFS." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/654.

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Problem difficulty for greedy best first search (GBFS) is not entirely understood, though existing work points to deep local minima and poor correlation between the h-values and the distance to goal as factors that have significant negative effect on the search effort. In this work, we show that there is a very strong exponential correlation between the depth of the single deepest local minima encountered in a search and the overall search effort. Furthermore, we find that the distribution of local minima depth changes dramatically based on the constrainedness of problems, suggesting an explanation for the previously observed heavy-tailed behavior in GBFS. In combinatorial search, a similar result led to the use of randomized restarts to escape deep subtrees with no solution and corresponding significant speed-ups. We adapt this method and propose a randomized restarting GBFS variant that improves GBFS performance by escaping deep local minima, and does so even in the presence of other, randomization-based, search enhancements.

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Feng, Han, Haixiang Zhang, and Javad Lavaei. "A Dynamical System Perspective for Escaping Sharp Local Minima in Equality Constrained Optimization Problems." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303907.

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Xue, Hui, and Zheng-Fan Wu. "BaKer-Nets: Bayesian Random Kernel Mapping Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/425.

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Recently, deep spectral kernel networks (DSKNs) have attracted wide attention. They consist of periodic computational elements that can be activated across the whole feature spaces. In theory, DSKNs have the potential to reveal input-dependent and long-range characteristics, and thus are expected to perform more competitive than prevailing networks. But in practice, they are still unable to achieve the desired effects. The structural superiority of DSKNs comes at the cost of the difficult optimization. The periodicity of computational elements leads to many poor and dense local minima in loss landscapes. DSKNs are more likely stuck in these local minima, and perform worse than expected. Hence, in this paper, we propose the novel Bayesian random Kernel mapping Networks (BaKer-Nets) with preferable learning processes by escaping randomly from most local minima. Specifically, BaKer-Nets consist of two core components: 1) a prior-posterior bridge is derived to enable the uncertainty of computational elements reasonably; 2) a Bayesian learning paradigm is presented to optimize the prior-posterior bridge efficiently. With the well-tuned uncertainty, BaKer-Nets can not only explore more potential solutions to avoid local minima, but also exploit these ensemble solutions to strengthen their robustness. Systematical experiments demonstrate the significance of BaKer-Nets in improving learning processes on the premise of preserving the structural superiority.

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Ashok, Dhananjay, Vineel Nagisetty, Christopher Srinivasa, and Vijay Ganesh. "A Solver + Gradient Descent Training Algorithm for Deep Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/246.

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We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP) solver, outperforming GD and variants in terms of accuracy, as well as resource and data efficiency for both regression and classification tasks. Our GD+Solver hybrid algorithm, called GDSolver, works as follows: given a DNN D as input, GDSolver invokes GD to partially train D until it gets stuck in a local minima, at which point GDSolver invokes an MILP solver to exhaustively search a region of the loss landscape around the weight assignments of D’s final layer parameters with the goal of tunnelling through and escaping the local minima. The process is repeated until desired accuracy is achieved. In our experiments, we find that GDSolver not only scales well to additional data and very large model sizes, but also outperforms all other competing methods in terms of rates of convergence and data efficiency. For regression tasks, GDSolver produced models that, on average, had 31.5% lower MSE in 48% less time, and for classification tasks on MNIST and CIFAR10, GDSolver was able to achieve the highest accuracy over all competing methods, using only 50% of the training data that GD baselines required.

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Yin, Su, and Jonathan Cagan. "A Pattern Search-Based Algorithm for Three-Dimensional Component Layout." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/dac-5582.

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Анотація:

Abstract A pattern search-based algorithm is introduced for efficient component layout optimization. The algorithm is applicable to general layout problems, where component geometry can be arbitrary, design goals can be multiple and spatial constraint satisfactions can be of different types. Extensions to pattern search are introduced to help the algorithm to converge to optimal solutions by escaping inferior local minima. The performance on all of the test problems shows that the algorithm runs one-to-two orders of magnitude faster than a robust simulated annealing-based algorithm for results with the same quality. The algorithm is further extended to solve a concurrent layout and routing problem, which demonstrates the ability of the algorithm to apply new pattern strategies in search and to include different objective functions in optimization.

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