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Relevant bibliographies by topics / Random Search Algorithm

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Academic literature on the topic 'Random Search Algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 28 May 2022

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Journal articles on the topic "Random Search Algorithm"

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Li, Jun. "A random dynamic search algorithm research." Journal of Computational Methods in Sciences and Engineering 19, no. 3 (July 17, 2019): 659–72. http://dx.doi.org/10.3233/jcm-193522.

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Vose, Michael D. "Logarithmic Convergence of Random Heuristic Search." Evolutionary Computation 4, no. 4 (December 1996): 395–404. http://dx.doi.org/10.1162/evco.1996.4.4.395.

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This paper speaks to the inherent emergent behavior of genetic search. For completeness and generality, a class of stochastic search algorithms, random heuristic search, is reviewed. A general convergence theorem for this class is then proved. Since the simple genetic algorithm (GA) is an instance of random heuristic search, a corollary is a result concerning GAs and time to convergence.

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Meenachi, Loganathan, and Srinivasan Ramakrishnan. "Random Global and Local Optimal Search Algorithm Based Subset Generation for Diagnosis of Cancer." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 3 (March 2, 2020): 249–61. http://dx.doi.org/10.2174/1573405614666180720152838.

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Background: Data mining algorithms are extensively used to classify the data, in which prediction of disease using minimal computation time plays a vital role. Objective: The aim of this paper is to develop the classification model from reduced features and instances. Methods: In this paper we proposed four search algorithms for feature selection the first algorithm is Random Global Optimal (RGO) search algorithm for searching the continuous, global optimal subset of features from the random population. The second is Global and Local Optimal (GLO) search algorithm for searching the global and local optimal subset of features from population. The third one is Random Local Optimal (RLO) search algorithm for generating random, local optimal subset of features from the random population. Finally the Random Global and Optimal (RGLO) search algorithm for searching the continuous, global and local optimal subset of features from the random population. RGLO search algorithm combines the properties of first three stated algorithm. The subsets of features generated from the proposed four search algorithms are evaluated using the consistency based subset evaluation measure. Instance based learning algorithm is applied to the resulting feature dataset to reduce the instances that are redundant or irrelevant for classification. The model developed using naïve Bayesian classifier from the reduced features and instances is validated with the tenfold cross validation. Results: Classification accuracy based on RGLO search algorithm using naïve Bayesian classifier is 94.82% for Breast, 97.4% for DLBCL, 98.83% for SRBCT and 98.89% for Leukemia datasets. Conclusion: The RGLO search based reduced features results in the high prediction rate with less computational time when compared with the complete dataset and other proposed subset generation algorithm.

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Kuo, Jim, Kevin Pan, Ni Li, and He Shen. "Wind Farm Yaw Optimization via Random Search Algorithm." Energies 13, no. 4 (February 16, 2020): 865. http://dx.doi.org/10.3390/en13040865.

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One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

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Liu, Bo. "Composite Differential Search Algorithm." Journal of Applied Mathematics 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/294703.

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Differential search algorithm (DS) is a relatively new evolutionary algorithm inspired by the Brownian-like random-walk movement which is used by an organism to migrate. It has been verified to be more effective than ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES. In this paper, we propose four improved solution search algorithms, namely “DS/rand/1,” “DS/rand/2,” “DS/current to rand/1,” and “DS/current to rand/2” to search the new space and enhance the convergence rate for the global optimization problem. In order to verify the performance of different solution search methods, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, the original algorithm when considering the quality of the solution obtained. However, these schemes cannot still achieve the best solution for all functions. In order to further enhance the convergence rate and the diversity of the algorithm, a composite differential search algorithm (CDS) is proposed in this paper. This new algorithm combines three new proposed search schemes including “DS/rand/1,” “DS/rand/2,” and “DS/current to rand/1” with three control parameters using a random method to generate the offspring. Experiment results show that CDS has a faster convergence rate and better search ability based on the 23 benchmark functions.

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KAELO, P., and M. M. ALI. "NUMERICAL STUDIES OF SOME GENERALIZED CONTROLLED RANDOM SEARCH ALGORITHMS." Asia-Pacific Journal of Operational Research 29, no. 02 (April 2012): 1250016. http://dx.doi.org/10.1142/s0217595912500169.

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This paper presents motivations and algorithmic details of some generalized controlled random search (CRS) algorithms for global optimization. It also carries out an extensive numerical study of the generalized CRS algorithms to demonstrate their superiorities over their original counterparts. The numerical study is carried out using a set of 50 test problems many of which are inspired by practical applications. Numerical experiments indicate that the generalized algorithms are considerably better than the previous versions. The algorithms are also compared with the DIRECT algorithm (Jones et al., 1993). The comparison shows that the generalized CRS algorithms are better than the DIRECT algorithm in high dimensional problems. Thus, they offer a reasonable alternative to many currently available stochastic algorithms, especially for problems requiring "direct search type" methods.

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Zhang, Qi, and Jiaqiao Hu. "Simulation Optimization Using Multi-Time-Scale Adaptive Random Search." Asia-Pacific Journal of Operational Research 36, no. 06 (December 2019): 1940014. http://dx.doi.org/10.1142/s0217595919400141.

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We propose a random search algorithm for seeking the global optimum of an objective function in a simulation setting. The algorithm can be viewed as an extension of the MARS algorithm proposed in Hu and Hu (2011) for deterministic optimization, which iteratively finds improved solutions by modifying and sampling from a parameterized probability distribution over the solution space. However, unlike MARS and many other algorithms in this class, which are often population-based, our method only requires a single candidate solution to be generated at each iteration. This is primarily achieved through an effective use of past sampling information by means of embedding multiple nested stochastic approximation type of recursions into the algorithm. We prove the global convergence of the algorithm under general conditions and discuss two special simulation noise cases of interest, in which we show that only one simulation replication run is needed for each sampled solution. A preliminary numerical study is also carried out to illustrate the algorithm.

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Zhang, Yu-Chao, Wan-Su Bao, Xiang Wang, and Xiang-Qun Fu. "Optimized quantum random-walk search algorithm for multi-solution search." Chinese Physics B 24, no. 11 (November 2015): 110309. http://dx.doi.org/10.1088/1674-1056/24/11/110309.

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Charilogis, Vasileios, Ioannis Tsoulos, Alexandros Tzallas, and Nikolaos Anastasopoulos. "An Improved Controlled Random Search Method." Symmetry 13, no. 11 (October 20, 2021): 1981. http://dx.doi.org/10.3390/sym13111981.

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A modified version of a common global optimization method named controlled random search is presented here. This method is designed to estimate the global minimum of multidimensional symmetric and asymmetric functional problems. The new method modifies the original algorithm by incorporating a new sampling method, a new termination rule and the periodical application of a local search optimization algorithm to the points sampled. The new version is compared against the original using some benchmark functions from the relevant literature.

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Li, Shou Tao, Li Na Li, and Gordon Lee. "A Robotic Swarm Searching Method for Unknown Environments Based on Foraging Behaviors." Applied Mechanics and Materials 461 (November 2013): 853–60. http://dx.doi.org/10.4028/www.scientific.net/amm.461.853.

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This paper proposes a novel method for a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics that are inspired by the foraging behavior in nature. First, the searching area is divided into several sub-regions using a target utility function, from which each robot can identify an area that should be initially searched. Then, a predatory strategy is used for searching in the sub-regions; this hybrid approach integrates a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a robot cannot find any target information in the sub-region, it uses a global random search algorithm; if the robot finds any target information in the sub-region, the DPSO search algorithm is used for a local search. The particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism as the searching progresses until the robots find the target position. Then, the robots fall back to a random searching mode and continue to search for other places that were not searched previously. In this searching strategy, the robots alternate between two searching algorithms until the whole sub-area is covered. During the searching process, the robots use a local communication mechanism to share map information and the DPSO parameters to reduce the communication burden and overcome hardware limitations.

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Dissertations / Theses on the topic "Random Search Algorithm"

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Rachakonda, Ravi Kanth. "Crew Rostering Problem: A Random Key Genetic Algorithm With Local Search." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1230931714.

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Rachakonda, Ravi Kanth. "Crew rostering problem a random key genetic algorithm with local search /." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1230931714.

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Weinig, Walter Theodore 1960. "Calibration of the soil moisture accounting model using the adaptive random search algorithm." Thesis, The University of Arizona, 1991. http://hdl.handle.net/10150/192059.

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Random search techniques are being applied to a variety of non-linear parameter estimation problems. Random search for global optimization has the potential to overcome many of the problems associated with direct or pattern search techniques. In this research, an adaptive random search algorithm was applied to a conceptual rainfall-runoff model to study the efficiency of the algorithm in locating an optimum set of model parameters. The goal of the study was to determine how changes in internal algorithm control variables and objective functions affected the efficiency of the algorithm. Results indicated that the value of internal control variables did not have a strong influence on algorithm efficiency. Neither objective function gave demonstrably better results in calibration runs. Variability in results due to the random number seed was observed. Recommendations for further research are presented.

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Long, Shun. "Adaptive Java optimisation using machine learning techniques." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/567.

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There is a continuing demand for higher performance, particularly in the area of scientific and engineering computation. In order to achieve high performance in the context of frequent hardware upgrading, software must be adaptable for portable performance. What is required is an optimising compiler that evolves and adapts itself to environmental change without sacrificing performance. Java has emerged as a dominant programming language widely used in a variety of application areas. However, its architectural independant design means that it is frequently unable to deliver high performance especially when compared to other imperative languages such as Fortran and C/C++. This thesis presents a language- and architecture-independant approach to achieve portable high performance. It uses the mapping notation introduced in the Unified Transformation Framework to specify a large optimisation space. A heuristic random search algorithm is introduced to explore this space in a feedback-directed iterative optimisation manner. It is then extended using a machine learning approach which enables the compiler to learn from its previous optimisations and apply the knowledge when necessary. Both the heuristic random search algorithm and the learning optimisation approach are implemented in a prototype Adaptive Optimisation Framework for Java (AOF-Java). The experimental results show that the heuristic random search algorithm can find, within a relatively small number of atttempts, good points in the large optimisation space. In addition, the learning optimisation approach is capable of finding good transformations for a given program from its prior experience with other programs.

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Jordanov, Dimitar Dimitrov. "Similarity Search in Document Collections." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236746.

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Hlavním cílem této práce je odhadnout výkonnost volně šířeni balík Sémantický Vektory a třída MoreLikeThis z balíku Apache Lucene. Tato práce nabízí porovnání těchto dvou přístupů a zavádí metody, které mohou vést ke zlepšení kvality vyhledávání.

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Singer, J. B. "Why solutions can be hard to find : a featural theory of cost for a local search algorithm on random satisfiability instances." Thesis, University of Edinburgh, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.661976.

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The local search algorithm WSAT is one of the most successful algorithms for solving the archetypal NP-complete problem of satisfiability (SAT). It is notably effective at solving RANDOM-3-SAT instances near the so-called "satisfiability threshold", which are thought to be universally hard. However, WSAT still shows a peak in search cost near the threshold and large variations in cost over different instances. Why are solutions to the threshold instances so hard to find using WSAT? What features characterise threshold instances which make them difficult for WSAT to solve? We make a number of significant contributions to the analysis of WSAT on these high-cost random instances, using the recently-introduced concept of the backbone of a SAT instance. The backbone is the set of literals which are implicates of and instance. We find that the number of solutions predicts the cost well for small-backbone instances but is much less relevant for the large-backbone instances which appear near the threshold and dominate in the overconstrained region. We undertake a detailed study of the behaviour of the algorithm during search and uncover some interesting patterns. These patterns lead us to introduce a measure of the backbone fragility of an instance, which indicates how persistent the backbone is as clauses are removed. We propose that high-cost random instances for WSAT are those with large backbones which are also backbone-fragile. We suggest that the decay in cost for WSAT beyond the satisfiability threshold, which has perplexed a number of researchers, is due to the decreasing backbone fragility. Our hypothesis makes three correct predictions. First, that a measure of the backbone robustness of an instance (the opposite to backbone fragility) is negatively correlated with the WSAT cost when other factors are controlled for. Second, that backbone-minimal instances (which are 3-SAT instances altered so as to be more backbone-fragile) are unusually hard for WSAT. Third, that the clauses most often unsatisfied during search are those whose deletion has the most effect on the backbone.

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Kopřiva, Jan. "Srovnání algoritmů při řešení problému obchodního cestujícího." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-222126.

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The Master Thesis deals with logistic module innovation of information system ERP. The principle of innovation is based on implementation of heuristic algorithms which solve Travel Salesman Problems (TSP). The software MATLAB is used for analysis and tests of these algorithms. The goal of Master Thesis is the comparison of selections algorithm, which are suitable for economic purposes (accuracy of solution, speed of calculation and memory demands).

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Deng, Qichen. "Antenna Optimization in Long-Term Evolution Networks." Thesis, KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-119147.

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The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization Algorithm, Random Search Algorithm, Modied Steepest Descent Algorithm and Multi-Objective Genetic Algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report.

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Jacmenovic, Dennis, and dennis_jacman@yahoo com au. "Optimisation of Active Microstrip Patch Antennas." RMIT University. Electrical and Computer Engineering, 2004. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20060307.144507.

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This thesis presents a study of impedance optimisation of active microstrip patch antennas to multiple frequency points. A single layered aperture coupled microstrip patch antenna has been optimised to match the source reflection coefficient of a transistor in designing an active antenna. The active aperture coupled microstrip patch antenna was optimised to satisfy Global Positioning System (GPS) frequency specifications. A rudimentary aperture coupled microstrip patch antenna consists of a rectangular antenna element etched on the top surface of two dielectric substrates. The substrates are separated by a ground plane and a microstrip feed is etched on the bottom surface. A rectangular aperture in the ground plane provides coupling between the feed and the antenna element. This type of antenna, which conveniently isolates any circuit at the feed from the antenna element, is suitable for integrated circuit design and is simple to fabricate. An active antenna design directly couples an antenna to an active device, therefore saving real estate and power. This thesis focuses on designing an aperture coupled patch antenna directly coupled to a low noise amplifier as part of the front end of a GPS receiver. In this work an in-house software package, dubbed ACP by its creator Dr Rod Waterhouse, for calculating aperture coupled microstrip patch antenna performance parameters was linked to HP-EEsof, a microwave computer aided design and simulation package by Hewlett-Packard. An ANSI C module in HP-EEsof was written to bind the two packages. This process affords the client the benefit of powerful analysis tools offered in HP-EEsof and the fast analysis of ACP for seamless system design. Moreover, the optimisation algorithms in HP-EEsof were employed to investigate which algorithms are best suited for optimising patch antennas. The active antenna design presented in this study evades an input matching network, which is accomplished by designing the antenna to represent the desired source termination of a transistor. It has been demonstrated that a dual-band microstrip patch antenna can be successfully designed to match the source reflection coefficient, avoiding the need to insert a matching network. Maximum power transfer in electrical circuits is accomplished by matching the impedance between entities, which is generally acheived with the use of a matching network. Passive matching networks employed in amplifier design generally consist of discrete components up to the low GHz frequency range or distributed elements at greater frequencies. The source termination for a low noise amplifier will greatly influence its noise, gain and linearity which is controlled by designing a suitable input matching network. Ten diverse search methods offered in HP-EEsof were used to optimise an active aperture coupled microstrip patch antenna. This study has shown that the algorithms based on the randomised search techniques and the Genetic algorithm provide the most robust performance. The optimisation results were used to design an active dual-band antenna.

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Esswein, Lance C. "Genetic algorithm design and testing of a random element 3-D 2.4 GHZ phased array transmit antenna constructed of commercial RF microchips." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FEsswein.pdf.

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Thesis (M.S. in Physics)--Naval Postgraduate School, June 2003.
Thesis advisor(s): Michael Melich, David Jenn, Rodney Johnson. Includes bibliographical references (p. 113-115). Also available online.

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Books on the topic "Random Search Algorithm"

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Mahmoud, Hosam M. Evolution of random search trees. New York: Wiley, 1992.

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Tarasenko, Georgiĭ Stepanovich. Stochastic optimization in the Soviet Union: Random search algorithms. Falls Church, Va. (7700 Leesburg Pike, #250, Falls Church 22043): Delphic Associates, 1985.

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P, Dhawan Atam, and United States. National Aeronautics and Space Administration., eds. Genetic algorithms as global random search methods. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Tarasenko, George. Stochastic Optimization in the Soviet Union: Random Search Algorithms. Delphic Assoc./Larry Gwensberg, 1986.

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Book chapters on the topic "Random Search Algorithm"

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Biernacki, Arkadiusz. "Numerical Evaluation of the Random Walk Search Algorithm." In Man-Machine Interactions, 533–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00563-3_56.

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Gao, Hang, Tiegang Gao, and Renhong Cheng. "Random Selective Image Encryption Algorithm Based on Chaos Search." In Communications in Computer and Information Science, 92–105. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2260-9_12.

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Di Pillo, Gianni, Stefano Lucidi, Laura Palagi, and Massimo Roma. "A Controlled Random Search Algorithm with Local Newton-type Search for Global Optimization." In Applied Optimization, 143–59. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3279-4_10.

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Wenhua, Ji, and Zhou Chang. "An Advanced Algorithm for Image Segmentation by Random Seed Region Search." In Advances in Intelligent and Soft Computing, 251–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25188-7_30.

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Zhang, Guangjian, and Enhao Zhang. "A Random Opposition-Based Sparrow Search Algorithm for Path Planning Problem." In Artificial Intelligence, 408–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93049-3_34.

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Zhang, Xiaoming, Jiakang He, and Qing Shen. "Research on Global Competition and Acoustic Search Algorithm Based on Random Attraction." In Communications in Computer and Information Science, 185–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7984-4_15.

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Hodashinsky, Ilya, Konstantin Sarin, and Artyom Slezkin. "Efficient Feature Selection Algorithm Based on Population Random Search with Adaptive Memory Strategies." In Advances in Intelligent Systems and Computing, 321–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01818-4_32.

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Leonhart, Pablo F., and Márcio Dorn. "A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking." In Applications of Evolutionary Computation, 360–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16692-2_24.

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Velamati, Sruthin, and V. Padmaja. "Policy Space Exploration for Linear Quadratic Regulator (LQR) Using Augmented Random Search (ARS) Algorithm." In Lecture Notes in Networks and Systems, 791–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3172-9_74.

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Qu, Yuan, and Zhong-ping Jiang. "Bi-Level Programming Model and Taboo Search Algorithm in Industrial Location Under the Condition of Random Price." In Emerging Technologies for Information Systems, Computing, and Management, 395–403. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7010-6_45.

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Conference papers on the topic "Random Search Algorithm"

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Gu, Jiadong, and Defeng Wu. "A random distribution harmony search algorithm." In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377498.

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Al-Muhammed, Muhammed Jassem, and Raed Abu Zitar. "Random-guided search algorithm for complex functions." In 2017 10th Jordanian International Electrical and Electronics Engineering Conference (JIEEEC). IEEE, 2017. http://dx.doi.org/10.1109/jieeec.2017.8051412.

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Mekhilef, Mounib. "Twinkling a Random Search Algorithm for Design Optimization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85305.

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In this paper we are going to show that applying the twinkling technique on a naive random search algorithm is frequently more powerful than any algorithm using specific research techniques unless they use information provided by the gradient or the Hessian. In order to illustrate this result we have made the choice of the study of a mechanical system characterized by the non-linear nature of the optimization space. This system is basically an open kinematics chain that represents a robot which has to go through various different trajectories defined by a set of temporally equidistant points. In fact, we are going to show that the genetic algorithm, the simulated annealing algorithm, the particle swarm algorithm, the random search algorithm, need to use comparatively, a huge number of function evaluations in order to perform the same result quality.

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Lins, Robson, Diogo B. Henriques, Emerson Lima, and Silvio Melo. "Quasi-Random Search algorithm for fast motion estimation." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638057.

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Aggarwal, A., and R. Anderson. "A random NC algorithm for depth first search." In the nineteenth annual ACM conference. New York, New York, USA: ACM Press, 1987. http://dx.doi.org/10.1145/28395.28430.

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Guo, Qing, Yangjun Gao, Lijie Cui, and Jiakui Zhang. "Cuckoo search algorithm based on three random walks." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322923.

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Peddapati, Sankar, and K. K. Phanisri Kruthiventi. "A New Random Search Algorithm: Multiple Solution Vector Approach." In 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE, 2016. http://dx.doi.org/10.1109/iacc.2016.43.

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Emary, E., Hossam M. Zawbaa, and Mustafa Abdul Salam. "A proposed whale search algorithm with adaptive random walk." In 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2017. http://dx.doi.org/10.1109/iccp.2017.8117000.

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Kumar, Rajeeva, and David Hyland. "Tuning of A Random Search Algorithm for Controller Design." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4762.

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Sheng-Jie Deng, Yu-Ren Zhou, Hua-Qing Min, and Jing-Hui Zhu. "Random search algorithm for 2×2 matrices multiplication problem." In 2010 Third International Workshop on Advanced Computational Intelligence (IWACI). IEEE, 2010. http://dx.doi.org/10.1109/iwaci.2010.5585184.

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Reports on the topic "Random Search Algorithm"

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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,

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