Artículos de revistas: "Random Search Algorithm"

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Li, Jun. "A random dynamic search algorithm research". Journal of Computational Methods in Sciences and Engineering 19, n.º 3 (17 de julio de 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, n.º 4 (diciembre de 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 y 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, n.º 3 (2 de marzo de 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 y He Shen. "Wind Farm Yaw Optimization via Random Search Algorithm". Energies 13, n.º 4 (16 de febrero de 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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5

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. y M. M. ALI. "NUMERICAL STUDIES OF SOME GENERALIZED CONTROLLED RANDOM SEARCH ALGORITHMS". Asia-Pacific Journal of Operational Research 29, n.º 02 (abril de 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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7

Zhang, Qi y Jiaqiao Hu. "Simulation Optimization Using Multi-Time-Scale Adaptive Random Search". Asia-Pacific Journal of Operational Research 36, n.º 06 (diciembre de 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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8

Zhang, Yu-Chao, Wan-Su Bao, Xiang Wang y Xiang-Qun Fu. "Optimized quantum random-walk search algorithm for multi-solution search". Chinese Physics B 24, n.º 11 (noviembre de 2015): 110309. http://dx.doi.org/10.1088/1674-1056/24/11/110309.

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Charilogis, Vasileios, Ioannis Tsoulos, Alexandros Tzallas y Nikolaos Anastasopoulos. "An Improved Controlled Random Search Method". Symmetry 13, n.º 11 (20 de octubre de 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 y Gordon Lee. "A Robotic Swarm Searching Method for Unknown Environments Based on Foraging Behaviors". Applied Mechanics and Materials 461 (noviembre de 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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11

Alava, Mikko, John Ardelius, Erik Aurell, Petteri Kaski, Supriya Krishnamurthy, Pekka Orponen y Sakari Seitz. "Circumspect descent prevails in solving random constraint satisfaction problems". Proceedings of the National Academy of Sciences 105, n.º 40 (1 de octubre de 2008): 15253–57. http://dx.doi.org/10.1073/pnas.0712263105.

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We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

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12

Lei, Ma, Du Jiang-Feng, Li Yun, Li Hui, Kwek L. C. y Oh C. H. "White Noise in Quantum Random Walk Search Algorithm". Chinese Physics Letters 23, n.º 4 (30 de marzo de 2006): 779–82. http://dx.doi.org/10.1088/0256-307x/23/4/005.

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13

Zhang, Yu-Chao, Wan-Su Bao, Xiang Wang y Xiang-Qun Fu. "Decoherence in optimized quantum random-walk search algorithm". Chinese Physics B 24, n.º 8 (agosto de 2015): 080307. http://dx.doi.org/10.1088/1674-1056/24/8/080307.

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14

Kazakovtsev, Lev. "Random Search Algorithm for the Generalized Weber Problem". Journal of Software Engineering and Applications 05, n.º 12 (2012): 59–65. http://dx.doi.org/10.4236/jsea.2012.512b013.

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15

HU, Jinglu, Kotaro HIRASAWA y Hiroyuki MIYAZAKI. "An Adaptive Random Search Algorithm with Tuning Capabilities". Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2003 (5 de mayo de 2003): 148–53. http://dx.doi.org/10.5687/sss.2003.148.

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16

Li, Guanghui y Chongqi Zhang. "Random search algorithm for optimal mixture experimental design". Communications in Statistics - Theory and Methods 47, n.º 6 (22 de septiembre de 2017): 1413–22. http://dx.doi.org/10.1080/03610926.2017.1321122.

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17

Peck, Charles C. y Atam P. Dhawan. "Genetic Algorithms as Global Random Search Methods: An Alternative Perspective". Evolutionary Computation 3, n.º 1 (marzo de 1995): 39–80. http://dx.doi.org/10.1162/evco.1995.3.1.39.

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Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

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18

Sakiyama, Tomoko y Yukio-Pegio Gunji. "Emergence of an optimal search strategy from a simple random walk". Journal of The Royal Society Interface 10, n.º 86 (6 de septiembre de 2013): 20130486. http://dx.doi.org/10.1098/rsif.2013.0486.

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In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.

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19

Watson, J. P., L. D. Whitley y A. E. Howe. "Linking Search Space Structure, Run-Time Dynamics, and Problem Difficulty: A Step Toward Demystifying Tabu Search". Journal of Artificial Intelligence Research 24 (1 de agosto de 2005): 221–61. http://dx.doi.org/10.1613/jair.1576.

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Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.

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20

Wang, Lijin y Yiwen Zhong. "Cuckoo Search Algorithm with Chaotic Maps". Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/715635.

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Cuckoo search algorithm is a novel nature-inspired optimization technique based on the obligate brood parasitic behavior of some cuckoo species. It iteratively employs Lévy flights random walk with a scaling factor and biased/selective random walk with a fraction probability. Unfortunately, these two parameters are used in constant value schema, resulting in a problem sensitive to solution quality and convergence speed. In this paper, we proposed a variable value schema cuckoo search algorithm with chaotic maps, called CCS. In CCS, chaotic maps are utilized to, respectively, define the scaling factor and the fraction probability to enhance the solution quality and convergence speed. Extensive experiments with different chaotic maps demonstrate the improvement in efficiency and effectiveness.

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MANGAL, MANISH y MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS". International Journal on Artificial Intelligence Tools 16, n.º 01 (febrero de 2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.

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This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.

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Jiang, Xiangyuan y Shuai Li. "BAS: Beetle Antennae Search Algorithm for Optimization Problems". International Journal of Robotics and Control 1, n.º 1 (25 de abril de 2018): 1. http://dx.doi.org/10.5430/ijrc.v1n1p1.

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Meta-heuristic algorithms have become very popular because of powerful performance on the optimization problem. A new algorithm called beetle antennae search algorithm (BAS) is proposed in the paper inspired by the searching behavior of long-horn beetles. The BAS algorithm imitates the function of antennae and the random walking mechanism of beetles in nature, and then two main steps of detecting and searching are implemented. Finally, the algorithm is benchmarked on 2 well-known test functions, in which the numerical results validate the efficacy of the proposed BAS algorithm.

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23

Budiman, Mohammad Andri y Dian Rachmawati. "Using random search and brute force algorithm in factoring the RSA modulus". Data Science: Journal of Computing and Applied Informatics 2, n.º 1 (1 de febrero de 2018): 45–52. http://dx.doi.org/10.32734/jocai.v2.i1-91.

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Abstract. The security of the RSA cryptosystem is directly proportional to the size of its modulus, n. The modulus n is a multiplication of two very large prime numbers, notated as p and q. Since modulus n is public, a cryptanalyst can use factorization algorithms such as Euler’s and Pollard’s algorithms to derive the private keys, p and q. Brute force is an algorithm that searches a solution to a problem by generating all the possible candidate solutions and testing those candidates one by one in order to get the most relevant solution. Random search is a numerical optimization algorithm that starts its search by generating one candidate solution randomly and iteratively compares it with other random candidate solution in order to get the most suitable solution. This work aims to compare the performance of brute force algorithm and random search in factoring the RSA modulus into its two prime factors by experimental means in Python programming language. The primality test is done by Fermat algorithm and the sieve of Eratosthenes.

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Pham, Duc-Truong, Maria M. Suarez-Alvarez y Yuriy I. Prostov. "Random search with k -prototypes algorithm for clustering mixed datasets". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467, n.º 2132 (9 de marzo de 2011): 2387–403. http://dx.doi.org/10.1098/rspa.2010.0594.

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A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k -prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k -prototypes algorithm. The BA works with elite and good solutions, and continues to look for other possible extrema solutions keeping the number of testing points constant. However, the improvement of promising solutions by the BA may be time-consuming because it is based on random neighbourhood search. On the other hand, an application of the k -prototypes algorithm to a promising solution may be very effective because it improves the solution at each iteration. The RANKPRO algorithm balances two objectives: it explores the search space effectively owing to random selection of new solutions, and improves promising solutions fast owing to employment of the k -prototypes algorithm. The efficiency of the new algorithm is demonstrated by clustering several datasets. It is shown that in the majority of the considered datasets when the average number of iterations that the k -prototypes algorithm needs to converge is over 10, the RANKPRO algorithm is more efficient than the k -prototypes algorithm.

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Ouyang, Chengtian, Yaxian Qiu y Donglin Zhu. "Adaptive Spiral Flying Sparrow Search Algorithm". Scientific Programming 2021 (26 de agosto de 2021): 1–16. http://dx.doi.org/10.1155/2021/6505253.

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The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.

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Zedadra, Ouarda, Antonio Guerrieri y Hamid Seridi. "LFA: A Lévy Walk and Firefly-Based Search Algorithm: Application to Multi-Target Search and Multi-Robot Foraging". Big Data and Cognitive Computing 6, n.º 1 (21 de febrero de 2022): 22. http://dx.doi.org/10.3390/bdcc6010022.

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In the literature, several exploration algorithms have been proposed so far. Among these, Lévy walk is commonly used since it is proved to be more efficient than the simple random-walk exploration. It is beneficial when targets are sparsely distributed in the search space. However, due to its super-diffusive behavior, some tuning is needed to improve its performance, specifically when targets are clustered. Firefly algorithm is a swarm intelligence-based algorithm useful for intensive search, but its exploration rate is very limited. An efficient and reliable search could be attained by combining the two algorithms since the first one allows exploration space, and the second one encourages its exploitation. In this paper, we propose a swarm intelligence-based search algorithm called Lévy walk and Firefly-based Algorithm (LFA), which is a hybridization of the two aforementioned algorithms. The algorithm is applied to Multi-Target Search and Multi-Robot Foraging. Numerical experiments to test the performances are conducted on the robotic simulator ARGoS. A comparison with the original firefly algorithm proves the goodness of our contribution.

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HONMA, Noriyasu, Mitsuo SATO y Hiroshi TAKEDA. "Learning Algorithm of Random Search for Optimal Control Inputs". Transactions of the Society of Instrument and Control Engineers 29, n.º 9 (1993): 1086–93. http://dx.doi.org/10.9746/sicetr1965.29.1086.

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Li, Yun, Lei Ma y Jie Zhou. "Gate imperfection in the quantum random-walk search algorithm". Journal of Physics A: Mathematical and General 39, n.º 29 (5 de julio de 2006): 9309–19. http://dx.doi.org/10.1088/0305-4470/39/29/021.

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Sawyerr, B. A., A. O. Adewumi y M. M. Ali. "Real-coded genetic algorithm with uniform random local search". Applied Mathematics and Computation 228 (febrero de 2014): 589–97. http://dx.doi.org/10.1016/j.amc.2013.11.097.

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Salcedo, R., M. J. Gonçalves y S. Feyo de Azevedo. "An improved random-search algorithm for non-linear optimization". Computers & Chemical Engineering 14, n.º 10 (octubre de 1990): 1111–26. http://dx.doi.org/10.1016/0098-1354(90)85007-w.

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Křivý, Ivan y Josef Tvrdík. "The controlled random search algorithm in optimizing regression models". Computational Statistics & Data Analysis 20, n.º 2 (agosto de 1995): 229–34. http://dx.doi.org/10.1016/0167-9473(95)90127-2.

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Al-Muhammed, Muhammed Jassem y Raed Abu Zitar. "Probability-directed random search algorithm for unconstrained optimization problem". Applied Soft Computing 71 (octubre de 2018): 165–82. http://dx.doi.org/10.1016/j.asoc.2018.06.043.

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Bernard, Pierre y Claude Bonnemoy. "An algorithm for spectral factorization using random search techniques". Probabilistic Engineering Mechanics 4, n.º 2 (junio de 1989): 66–73. http://dx.doi.org/10.1016/0266-8920(89)90011-8.

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Ye, Tao y Shivkumar Kalyanaraman. "A recursive random search algorithm for network parameter optimization". ACM SIGMETRICS Performance Evaluation Review 32, n.º 3 (diciembre de 2004): 44–53. http://dx.doi.org/10.1145/1052305.1052306.

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Ouyang, Chengtian, Donglin Zhu y Fengqi Wang. "A Learning Sparrow Search Algorithm". Computational Intelligence and Neuroscience 2021 (6 de agosto de 2021): 1–23. http://dx.doi.org/10.1155/2021/3946958.

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This paper solves the drawbacks of traditional intelligent optimization algorithms relying on 0 and has good results on CEC 2017 and benchmark functions, which effectively improve the problem of algorithms falling into local optimality. The sparrow search algorithm (SSA) has significant optimization performance, but still has the problem of large randomness and is easy to fall into the local optimum. For this reason, this paper proposes a learning sparrow search algorithm, which introduces the lens reverse learning strategy in the discoverer stage. The random reverse learning strategy increases the diversity of the population and makes the search method more flexible. In the follower stage, an improved sine and cosine guidance mechanism is introduced to make the search method of the discoverer more detailed. Finally, a differential-based local search is proposed. The strategy is used to update the optimal solution obtained each time to prevent the omission of high-quality solutions in the search process. LSSA is compared with CSSA, ISSA, SSA, BSO, GWO, and PSO in 12 benchmark functions to verify the feasibility of the algorithm. Furthermore, to further verify the effectiveness and practicability of the algorithm, LSSA is compared with MSSCS, CSsin, and FA-CL in CEC 2017 test function. The simulation results show that LSSA has good universality. Finally, the practicability of LSSA is verified by robot path planning, and LSSA has good stability and safety in path planning.

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Abu Doush, Iyad y Eugene Santos. "Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm". Journal of Intelligent Systems 30, n.º 1 (30 de mayo de 2020): 1–17. http://dx.doi.org/10.1515/jisys-2019-0101.

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Abstract Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future.

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37

Elkhechafi, Mariam, Hanaa Hachimi y Youssfi Elkettani. "A new hybrid cuckoo search and firefly optimization". Monte Carlo Methods and Applications 24, n.º 1 (1 de marzo de 2018): 71–77. http://dx.doi.org/10.1515/mcma-2018-0003.

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Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms (CS-FA).

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Xie, Shaohua, Shan He y Jing Cheng. "Research on improved sparrow algorithm based on random walk". Journal of Physics: Conference Series 2254, n.º 1 (1 de abril de 2022): 012051. http://dx.doi.org/10.1088/1742-6596/2254/1/012051.

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Abstract The optimization problem is a hot issue in today’s science and engineering research. The sparrow algorithm has the advantages of simple structure, few control parameters and high solution accuracy, and has been widely used in the research of optimization problems. Purposing at the problem that the sparrow search algorithm (SSA) can’t take into account the global and local optimization, an improved sparrow algorithm based on random walk strategy is proposed. After the sparrow search, the random walk is used to perturb the optimal sparrow to demonstrate its search-ability. At the original of the iteration, the random walk boundary is large, which is favourable to demonstrate the whole search-ability. After several iterations, the walk boundary becomes smaller, which improves the local search-ability of the best location of the algorithm. Taking the convergence speed, algorithm stability and convergence precision as evaluation indicators, the improved Sparrow Algorithm (RWSSA) is verified by 4 unimodal functions and 5 multimodal classical test functions, and compared with the traditional Sparrow algorithm. The experimental results show that the capacity of the improved sparrow algorithm based on random walk is significantly improved. At the same time, RWSSA is put into practice the power prediction problem, which checkouts the feasibility of RWSSA in actual engineering problems.

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39

Wang, Xianpeng, Xinglu Ma, Xiaoxu Li, Xiaoyu Ma y Chunxu Li. "Target-biased informed trees: sampling-based method for optimal motion planning in complex environments". Journal of Computational Design and Engineering 9, n.º 2 (abril de 2022): 755–71. http://dx.doi.org/10.1093/jcde/qwac025.

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Abstract Aiming at the problem that the progressively optimized Rapidly-exploring Random Trees Star (RRT*) algorithm generates a large number of redundant nodes, which causes slow convergence and low search efficiency in high-dimensional and complex environments. In this paper we present Target-biased Informed Trees (TBIT*), an improved RRT* path planning algorithm based on target-biased sampling strategy and heuristic optimization strategy. The algorithm adopts a combined target bias strategy in the search phase of finding the initial path to guide the random tree to grow rapidly toward the target direction, thereby reducing the generation of redundant nodes and improving the search efficiency of the algorithm; after the initial path is searched, heuristic sampling is used to optimize the initial path instead of optimizing the random tree, which can benefit from reducing useless calculations, and improve the convergence capability of the algorithm. The experimental results show that the algorithm proposed in this article changes the randomness of the algorithm to a certain extent, and the search efficiency and convergence capability in complex environments have been significantly improved, indicating that the improved algorithm is feasible and efficient.

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40

Nelson, John y J. Douglas Brodie. "Comparison of a random search algorithm and mixed integer programming for solving area-based forest plans". Canadian Journal of Forest Research 20, n.º 7 (1 de julio de 1990): 934–42. http://dx.doi.org/10.1139/x90-126.

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An area-based forest plan is formulated and solved by mixed integer programming and a random search algorithm. This is a computationally difficult problem because operational and environmental constraints require that harvest units and road projects be defined as strict binary variables. It was found that the random search algorithm could easily identify several solutions with objective function values within 10% of the true optimum. The best solution found was within 3% of the optimum. The random search algorithm is simple and can be readily implemented on the microcomputer. It is concluded that the random search algorithm is an effective technique for generating acceptable alternatives to complex area-based planning problems.

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41

Fu, Huimin, Yang Xu, Shuwei Chen y Jun Liu. "Improving WalkSAT for Random 3-SAT Problems". JUCS - Journal of Universal Computer Science 26, n.º 2 (28 de febrero de 2020): 220–43. http://dx.doi.org/10.3897/jucs.2020.013.

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Stochastic local search (SLS) algorithms are well known for their ability to efficiently find models of random instances of the Boolean satisfiability (SAT) problems. One of the most famous SLS algorithms for SAT is called WalkSAT, which has wide influence and performs well on most of random 3-SAT instances. However, the performance of WalkSAT lags far behind on random 3-SAT instances equal to or greater than the phase transition ratio. Motivated by this limitation, in the present work, firstly an allocation strategy is introduced and utilized in WalkSAT to determine the initial assignment, leading to a new algorithm called WalkSATvav. The experimental results show that WalkSATvav significantly outperforms the state-of-the-art SLS solvers on random 3-SAT instances at the phase transition for SAT Competition 2017. However, WalkSATvav cannot rival its competitors on random 3-SAT instances greater than the phase transition ratio. Accordingly, WalkSATvav is further improved for such instances by utilizing a combination of an improved genetic algorithm and an improved ant colony algorithm, which complement each other in guiding the search direction. The resulting algorithm, called WalkSATga, is far better than WalkSAT and significantly outperforms some previous known SLS solvers on random 3-SAT instances greater than the phase transition ratio from SAT Competition 2017. Finally, a new SAT solver called WalkSATlg, which combines WalkSATvav and WalkSATga, is proposed, which is competitive with the winner of random satisfiable category of SAT competition 2017 on random 3-SAT problem.

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42

Kou, Bin, Dongcheng Ren y Shijie Guo. "Geometric Parameter Identification of Medical Robot Based on Improved Beetle Antennae Search Algorithm". Bioengineering 9, n.º 2 (29 de enero de 2022): 58. http://dx.doi.org/10.3390/bioengineering9020058.

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To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add the random wandering behavior of the wolf colony algorithm to the search process of the beetle antennae search algorithm to strengthen its capability for local search. Following this, we improve the global convergence ability of the beetle antennae search algorithm by using the simulated annealing algorithm. We compare the accuracy of end positioning of the proposed algorithm with the frog-jumping algorithm and the beetle antennae search algorithm with variable step length through simulations. The results show that the proposed algorithm has a higher accuracy of convergence, and can significantly improve the accuracy of end positioning of the medical robot.

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43

Deghbouch, Hicham y Fatima Debbat. "Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks". Inteligencia Artificial 24, n.º 67 (20 de febrero de 2021): 18–35. http://dx.doi.org/10.4114/intartif.vol24iss67pp18-35.

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This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithmâ€™s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.

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44

Dehghani, Mohammad, Zeinab Montazeri, Gaurav Dhiman, O. P. Malik, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza, Ali Dehghani, Josep M. Guerrero y Lizeth Parra-Arroyo. "A Spring Search Algorithm Applied to Engineering Optimization Problems". Applied Sciences 10, n.º 18 (4 de septiembre de 2020): 6173. http://dx.doi.org/10.3390/app10186173.

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At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.

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45

Hashim, Norazlan, Zainal Salam, Nik Fasdi Nik Ismail y Dalina Johari. "New deterministic initialization method for soft computing global optimization algorithms". Indonesian Journal of Electrical Engineering and Computer Science 18, n.º 3 (1 de junio de 2020): 1607. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1607-1615.

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<span>The initialization stage12 of a Soft Computing (SC) algorithm is vital as it affects the success rate of algorithms in solving multi-peak global optimization problems. The individuals in an initial population, which are known as search agents, are often generated randomly using pseudo-random number generator (PRNG) due to unavailability of prior information on the location of global peak (GP). The random nature of the generated search agents causes uneven distribution of the initial population over the search space (SS), which may lead the search towards unpromising regions from the very beginning. This paper proposes a new deterministic initialization method (DIM) for SC algorithms where search agents are evenly fixed in the SS by using a simple deterministic formulation. The performance of the proposed DIM is then compared to the conventional PRNG and more recent quasi-random number generator (QRNG). An optimization case study is carried out using two popular SC algorithms which are the Particle Swarm Algorithm (PSO) and the Evolutionary Programming (EP), and three relatively new SC algorithms which are the Whale Optimization Algorithm (WOA), the Elephant Herding Optimization (EHO), and the Butterfly Optimization Algorithm (BOA). The optimization is done on various one-dimensional (1D) benchmark functions, as well as practical problems such as partial shading condition (PSC). Simulation results show that the proposed DIM successfully improved the performance of each SC algorithm under study in solving almost all tested functions with 99% success rate compared to 88.7% and 80.2% for the QRNG and PRNG approaches respectively. Furthermore, the WOA is the most reliable and robust among the five SC techniques under study with a success </span>

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46

Zhang, Jun, Ming Li y Hui Rong Xiao. "The Search on Mathematical Model of Evolutionary Computation". Applied Mechanics and Materials 687-691 (noviembre de 2014): 1568–72. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1568.

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Evolutionary algorithm is a random search method. In recent decades, various evolutionary algorithms emerge in endlessly by imitating population activity or the law of nature, so that evolutionary computation has been developed rapidly. The study of mathematical model about evolutionary algorithm is very lack. Only a small amount of research has been done. Through the study of the matrix model of evolutionary computation, this paper discusses the evolutionary process based on the matrix model in detail, and the working principle of matrix model and physical significance. The improvement suggestions of evolutionary algorithm have been given from the angle of the matrix model.

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47

Tunay, Mustafa y Rahib Abiyev. "Improved Hypercube Optimisation Search Algorithm for Optimisation of High Dimensional Functions". Mathematical Problems in Engineering 2022 (22 de abril de 2022): 1–13. http://dx.doi.org/10.1155/2022/6872162.

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This paper proposes a stochastic search algorithm called improved hypercube optimisation search (HOS+) to find a better solution for optimisation problems. This algorithm is an improvement of the hypercube optimisation algorithm that includes initialization, displacement-shrink and searching area modules. The proposed algorithm has a new random parameters (RP) module that uses two control parameters in order to prevent premature convergence and slow finishing and improve the search accuracy considerable. Many optimisation problems can sometimes cause getting stuck into an interior local optimal solution. HOS+ algorithm that uses a random module can solve this problem and find the global optimal solution. A set of experiments were done in order to test the performance of the algorithm. At first, the performance of the proposed algorithm is tested using low and high dimensional benchmark functions. The simulation results indicated good convergence and much better performance at the lowest of iterations. The HOS+ algorithm is compared with other meta heuristic algorithms using the same benchmark functions on different dimensions. The comparative results indicated the superiority of the HOS+ algorithm in terms of obtaining the best optimal value and accelerating convergence solutions.

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48

Cui, Zhe y Xingsheng Gu. "A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown". Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/621393.

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The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.

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Pan, Jun Hui, Hui Wang y Xiao Gang Yang. "A Random Particle Swarm Optimization Algorithm with Application". Advanced Materials Research 634-638 (enero de 2013): 3940–44. http://dx.doi.org/10.4028/www.scientific.net/amr.634-638.3940.

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To improve the efficiency of particle swarm optimization, a random particle swarm optimization algorithm is proposed on the basis of analyzing the search process of quantum particle swarm optimization algorithm. The proposed algorithm has only a parameter, and its search step length is controlled by a random variable value. In this model, the target position can be accurately tracked by the reasonable design of the control parameter. The experimental results of standard test function extreme optimization and clustering optimization show that the proposed algorithm is superior to the quantum particle swarm optimization and the common particle swarm optimization algorithm in optimization ability and optimization efficiency.

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50

Zhang, Li Hong y Shu Qian Chen. "Research of Hybrid Mobile Agent Routing in Wireless Sensor Network". Applied Mechanics and Materials 341-342 (julio de 2013): 1181–86. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.1181.

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The mobile agent route is essentially a multi-constraint optimization problem, Genetic Algorithms has fast random global search ability, but the feedback information of the system does not use and has the problem of low efficiency of finding exact solutions, propose a genetic hybrid ant colony algorithm for WSN mobile agent route. Use of the fast random global search capabilities of genetic algorithm to find better solutions, then the better solution replaced by the initial pheromone of the ant colony algorithm, finally use the advantages of convergence speed of ant colony algorithm to find the global optimal solution for mobile agent route. Simulation results show that the algorithm can find optimal mobile agent route in a relatively short time, relative to other routing algorithms, reducing network latency and average energy consumption, improving the speed and efficiency of data transfer.

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