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Journal articles on the topic 'Random search for optimum values'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Gvasalia, Badri, and Tamar Kvachadze. "Optimum Parameters of Cylindrical Springs Calculation of Values." Works of Georgian Technical University, no. 1(535) (March 25, 2025): 199–209. https://doi.org/10.36073/1512-0996-2025-1-199-209.

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Methods of calculating the values of optimal parameters of cylindrical springs are elaborated in the paper The issues raised are presented as the tasks of non -linear mathematical programming. The stochastic method is used to show the global extremum of purpose, in particular, the random search method to find extremum. Appropriate algorithms are processed, which is realized by the algorithmic language through Visual Basic.

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2

Nelson, John, and 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, no. 7 (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 t

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Valsta, Lauri T. "A comparison of numerical methods for optimizing even aged stand management." Canadian Journal of Forest Research 20, no. 7 (1990): 961–69. http://dx.doi.org/10.1139/x90-129.

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A two-species, whole-stand, deterministic growth model was combined with three optimization methods to derive management regimes for species composition, thinnings, and rotation age, with the objective of maximizing soil expectation value. The methods compared were discrete time – discrete state dynamic programming, direct search using the Hooke and Jeeves algorithm, and random search. Optimum solutions for each of the methods varied considerably, required unequal amounts of computational time, and were not equally stable. Dynamic programming located global optimal solutions but did not determ

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Lee, Kang Seok, and Jin Kyu Song. "Configuration Optimization of Truss Structures Using Harmony Search Heuristic Algorithm." Advanced Materials Research 26-28 (October 2007): 793–96. http://dx.doi.org/10.4028/www.scientific.net/amr.26-28.793.

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Most structural optimization methods are based on mathematical algorithms that require substantial gradient information. The selection of the starting values is also important to ensure that the algorithm converges to the global optimum. This paper describes a new structural configuration optimization method based on the harmony search (HS) meta-heuristic algorithm. The HS algorithm does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. A benchmark truss example is presented to demonstrate the effectiveness and robustnes

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Zhao, Ji, Yi Fu, and Juan Mei. "An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision." Journal of Algorithms & Computational Technology 14 (January 2020): 174830262097353. http://dx.doi.org/10.1177/1748302620973537.

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A novel dynamic cooperative random drift particle swarm optimization algorithm based on entire search history decision (CRDPSO) is reported. At each iteration, the positions and the fitness values of the evaluated solutions in the algorithm are stored by a binary space partitioning tree structure archive, which leads to a fast fitness function approximation. The mutation is adaptive and parameter-less because of the fitness function approximation enhancing the mutation strategy. The dynamic cooperation between the particles by using the context vector increases the population diversity helps t

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Pan, Ying, Dong Juan Xue, Tian Yi Gao, Li Bin Zhou, and Xiao Yu Xie. "A Modified Adaptive Genetic Algorithm for the Flexible Job-Shop Scheduling Problem." Applied Mechanics and Materials 401-403 (September 2013): 2037–43. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.2037.

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Combined with the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), a modified Adaptive Genetic Algorithm based on iterative generation and analysis of fitness values distribution is presented in this paper, which has both methods advantages. Instance simulation verifies that the FJSPs own characteristics are utilized in its solution by using the modified AGA, which overcomes traditional GAs limitation that initial stage of evolution is early and random search of medium-late stage is

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Savran, Melih, Mücahit Osman Türkan, Mustafa Öncül, and Levent Aydın. "Optimization of drilling parameters for minimizing delamination factor in biocomposites using multiple nonlinear neuro-regression and stochastic methods." Journal of Innovative Engineering and Natural Science 5, no. 1 (2024): 225–42. https://doi.org/10.61112/jiens.1564284.

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In this study, a novel design optimization strategy is proposed to enhance the drilling performance of HDPE/Washingtonia fiber biocomposites, considering operational parameters such as drill diameter (d), feed rate (f), and spindle speed (N). A detailed investigation utilizing multiple nonlinear neuro-regression analyses is conducted to predict the delamination factor (Fd), based on a dataset obtained from a literature study. 14 candidate mathematical functions are suggested for modelling, and their accuracy is assessed through R2training, R2testing, and R2validation metrics, and boundedness c

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ÖZDAMAR, LINET, and CHANDRA SEKHAR PEDAMALLU. "NEW SIMULATED ANNEALING ALGORITHMS FOR CONSTRAINED OPTIMIZATION." Asia-Pacific Journal of Operational Research 27, no. 03 (2010): 347–67. http://dx.doi.org/10.1142/s0217595910002740.

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We propose a Population based dual-sequence Non-Penalty Annealing algorithm (PNPA) for solving the general nonlinear constrained optimization problem. The PNPA maintains a population of solutions that are intermixed by crossover to supply a new starting solution for simulated annealing throughout the search. Every time the search gets stuck at a local optimum, this crossover procedure is triggered and simulated annealing search re-starts from a new subspace. In both the crossover and simulated annealing procedures, the objective function value and the total solution infeasibility degrees are t

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Sharma, Rakesh Chandmal, Sakshi Sharma, Sunil Kumar Sharma, Neeraj Sharma, and Gurpreet Singh. "Analysis of bio-dynamic model of seated human subject and optimization of the passenger ride comfort for three-wheel vehicle using random search technique." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 235, no. 1 (2021): 106–21. http://dx.doi.org/10.1177/1464419320983711.

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Ride comfort is the major concern to the roadway vehicle passengers, travelling in as it affects their health and efficiency to work. In the present study, a 9 DoF model of a three-wheel vehicle is developed with Lagrangian approach to investigate its ride behavior when subjected to random surface irregularities. The irregularities of the track are measured with a three-wheeled setup equipped with profilometer known as opto-coupler. The present model is validated in two ways, first by comparing the vertical-lateral PSD acceleration received from simulation and actual testing and second by comp

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Abramzon, Boris. "Numerical Optimization of the Thermoelectric Cooling Devices." Journal of Electronic Packaging 129, no. 3 (2006): 339–47. http://dx.doi.org/10.1115/1.2753959.

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The present study proposes a unified numerical approach to the problem of optimum design of the thermoelectric devices for cooling electronic components. The standard mathematical model of a single-stage thermoelectric cooler (TEC) with constant material properties is employed. The model takes into account the thermal resistances from the hot and cold sides of the TEC. Values of the main physical parameters governing the TEC performance (Seebeck coefficient, electrical resistance, and thermal conductance) are derived from the manufacturer catalog data on the maximum achievable temperature diff

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Fong, Simon, Suash Deb, Xin-She Yang, and Yan Zhuang. "Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/564829.

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Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperat

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Mashwani, Wali Khan, Ihsan Mehmood, Maharani Abu Bakar, and Ismail Koçcak. "A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems." Mathematical Problems in Engineering 2021 (February 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/6636918.

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In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutiona

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Martinovic, Goran, Ivan Aleksi, and Alfonzo Baumgartner. "Single-Commodity Vehicle Routing Problem with Pickup and Delivery Service." Mathematical Problems in Engineering 2008 (2008): 1–17. http://dx.doi.org/10.1155/2008/697981.

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We present a novel variation of the vehicle routing problem (VRP). Single commodity cargo with pickup and delivery service is considered. Customers are labeled as either cargo sink or cargo source, depending on their pickup or delivery demand. This problem is called a single commodity vehicle routing problem with pickup and delivery service (1-VRPPD). 1-VRPPD deals with multiple vehicles and is the same as the single-commodity traveling salesman problem (1-PDTSP) when the number of vehicles is equal to 1. Since 1-VRPPD specializes VRP, it is hard in the strong sense. Iterative modified simulat

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Altay, Osman, and Elif Varol Altay. "A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection." PeerJ Computer Science 9 (August 22, 2023): e1526. http://dx.doi.org/10.7717/peerj-cs.1526.

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Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic

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15

Dubey, Sandhya Parasnath, S. Balaji, N. Gopalakrishna Kini, and M. Sathish Kumar. "A Novel Framework for Ab Initio Coarse Protein Structure Prediction." Advances in Bioinformatics 2018 (June 20, 2018): 1–17. http://dx.doi.org/10.1155/2018/7607384.

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Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fi

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16

Yang, Yong, Yujie Fu, Dongyang Lu, Honghui Xiang, and Kaijun Xu. "Three-Dimensional Unmanned Aerial Vehicle Trajectory Planning Based on the Improved Whale Optimization Algorithm." Symmetry 16, no. 12 (2024): 1561. http://dx.doi.org/10.3390/sym16121561.

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The effective planning of UAV trajectories in a 3D environment presents a complex global optimization challenge that must account for numerous constraints, including urban settings, mountainous terrain, obstacles, no-fly zones, flight boundaries, travel distances, and trajectory change rates. This paper addresses the limitations of the whale optimization algorithm in 3D trajectory planning—specifically its slow convergence, low accuracy, and susceptibility to local optimum—by proposing an improved whale optimization algorithm. This enhancement incorporates an inverse learning mechanism to incr

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17

Srikakolapu, Jayadeep. "Distribution Static Compensator Using an Adaptive Observer Based Control Algorithm With Salp Swarm Optimization Algorithm." CPSS Transactions on Power Electronics and Applications 6, no. 1 (2021): 52–62. http://dx.doi.org/10.24295/cpsstpea.2021.00005.

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In this work, an adaptive observer supported fundamental extractor is developed to estimate the fundamental components of the load current for a three phase distribution static compensator (DSTATCOM) under nonlinear load. Main variations in the proposed work are the fundamental drawing out from the distorted load current and estimation of PI controller gains. With this observer, salp swarm optimization algorithm (SSOA) is used for estimation of DC PI controller and AC PI controller gains. The estimated gains are used for DC bus voltage and AC terminal voltage error minimization respectively. T

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Ahmad Basri, Muhamad Afdal, Shazlyn Milleana Shaharudin, Kismiantini, et al. "Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches." ISPRS International Journal of Geo-Information 10, no. 10 (2021): 689. http://dx.doi.org/10.3390/ijgi10100689.

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Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points

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Ahmad Basri, Muhamad Afdal, Shazlyn Milleana Shaharudin, Kismiantini, et al. "Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches." ISPRS International Journal of Geo-Information 10, no. 10 (2021): 689. http://dx.doi.org/10.3390/ijgi10100689.

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Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points

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20

Olivares, Rodrigo, Ricardo Soto, Broderick Crawford, et al. "Entropy–Based Diversification Approach for Bio–Computing Methods." Entropy 24, no. 9 (2022): 1293. http://dx.doi.org/10.3390/e24091293.

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Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensificat

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21

Huang, Xiao-Yu, Shuai Wang, Tong Lu, Hou-Min Li, Ke-Yang Wu, and Wei-Chao Deng. "Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation." Polymers 15, no. 2 (2023): 308. http://dx.doi.org/10.3390/polym15020308.

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The addition of rubber to concrete improves resistance to chloride ion attacks. Therefore, rapidly determining the chloride permeability coefficient (DCI) of rubber concrete (RC) can contribute to promotion in coastal areas. Most current methods for determining DCI of RC are traditional, which cannot account for multi-factorial effects and suffer from low prediction accuracy. Machine learning (ML) techniques have good non-linear learning capabilities and can consider the effects of multiple factors compared with traditional methods. However, ML models easily fall into the local optimum due to

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22

Alnowibet, Khalid Abdulaziz, Salem Mahdi, Ahmad M. Alshamrani, Karam M. Sallam, and Ali Wagdy Mohamed. "A Family of Hybrid Stochastic Conjugate Gradient Algorithms for Local and Global Minimization Problems." Mathematics 10, no. 19 (2022): 3595. http://dx.doi.org/10.3390/math10193595.

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This paper contains two main parts, Part I and Part II, which discuss the local and global minimization problems, respectively. In Part I, a fresh conjugate gradient (CG) technique is suggested and then combined with a line-search technique to obtain a globally convergent algorithm. The finite difference approximations approach is used to compute the approximate values of the first derivative of the function f. The convergence analysis of the suggested method is established. The comparisons between the performance of the new CG method and the performance of four other CG methods demonstrate th

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Loy, John. "New Search Strategies Successfully Optimize Retrieval of Clinically Sound Treatment Studies in EMBASE." Evidence Based Library and Information Practice 2, no. 2 (2007): 87. http://dx.doi.org/10.18438/b8zp4v.

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A review of:  Wong, Sharon S-L, Nancy L. Wilczynski, and R. Brian Haynes. “Developing Optimal Search Strategies for Detecting Clinically Sound Treatment Studies in EMBASE.” Journal of the Medical Library Association 94.1 (Jan. 2006): 41-47. 14 May 2007 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1324770.  Abstract  Objective – To develop and test the sensitivity and specificity, precision and accuracy of search strategies to retrieve clinically sound treatment studies in the EMBASE database.  Design – Analytical study. 

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24

Carpenter, Chris. "Numerical Simulation of Gas Lift Optimization Uses Genetic Algorithm." Journal of Petroleum Technology 74, no. 03 (2022): 65–67. http://dx.doi.org/10.2118/0322-0065-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 20254, “Numerical Simulation of Gas Lift Optimization Using Genetic Algorithm for a Middle East Oil Field: Feasibility Study,” by Mustafa AlJuboori and Mofazzal Hossain, SPE, Curtin University, and Omar Al-Fatlawi, SPE, University of Baghdad, et al. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. Optimal allocation of gas-injection rates in large fields through a gas lift network is a challenging task. Traditional gas li

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Wati, Vera, Yuliana Yuliana, Paradise Paradise, and Kusrini Kusrini. "TRAVELLING SALESMAN PROBLEM (TSP) OPTIMIZATION SEED DIS-TRIBUTION USING GENETIC ALGORITHM." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 8, no. 3 (2022): 359–68. http://dx.doi.org/10.33330/jurteksi.v8i3.1738.

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Abstract: Distribution is an important the business sector, the agricultural sector for distributing seeds to ensure the location of customers selling seeds. Problems that are often encountered seed distribution process are the efficiency of the time and distance distribution. Re search will build software entering initial location data and several dynamically added consumer agents. The distance parameter uses latitude-longitude integrated on google maps and detects varying store locations, the generation of chromosomes or the best distribution path with the minimum distance route. The heurist

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Huang, Gewen, Yuanhang Qi, Yanguang Cai, Yuhui Luo, and Helie Huang. "A Grey Wolf Optimizer Algorithm for Multi-Objective Cumulative Capacitated Vehicle Routing Problem Considering Operation Time." Biomimetics 9, no. 6 (2024): 331. http://dx.doi.org/10.3390/biomimetics9060331.

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In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a m

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Selvarani, P., and N. Malarvizhi. "Secure data in cloud with multimodal key generation." International Journal of Engineering & Technology 7, no. 1.7 (2018): 27. http://dx.doi.org/10.14419/ijet.v7i1.7.9382.

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Data Security is the Major problem in Cloud Computing. In order to overcome the data security problem the proposed technique utilizes effective data storage using biometric-based cryptographic authentication to support the user authentication for the cloud environment. For user authentication here we are considering iris and fingerprint. Initially the feature values are extracted from the iris and fingerprint using local binary pattern and Minutiae extraction respectively. Local binary pattern operator works with the eight neighbors of a pixel, using the value of this center pixel as a thresho

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Widiartha, I. Made, Anak Agung Ngurah Gunawan, E. R. Ngurah Agus Sanjaya, and Kartika Sari. "A Hybrid Method of Backpropagation and Particle Swarm Optimization for Enhancing Accuracy Performance." Current Journal of Applied Science and Technology 42, no. 6 (2023): 10–18. http://dx.doi.org/10.9734/cjast/2023/v42i64072.

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Aims: Backpropagation is an algorithm for adjusting the weight of neural networks in the training stage. The performance of backpropagation has proven superior in optimizing the weight of neural networks; however, this method needs improvement in the initiation stage, where the random process creates local optimal solution. Applying an algorithm based on the global search is an alternative to solve the drawback of backpropagation. One global search method with superior performance is particle swarm optimization. In this research, we apply the hybridization of backpropagation and particle swarm

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Vaidogas, E. R. "ON RELIABILITY-BASED STRUCTURAL OPTIMISATION USING STOCHASTIC QUASIGRADIENT METHODS/ZUR ZUVERLÄSSIGKEITSTHEORETISCH GESTÜTZTEN TRAGWERKS-OPTIMIERUNG MIT VERFAHREN DER STOCHASTISCHEN QUASIGRA-DIENTEN." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 1, no. 2 (1995): 43–64. http://dx.doi.org/10.3846/13921525.1995.10531512.

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Methodical aspects of the reliability-based structural optimisation using stochastic quasigradient methods are considered. For an example of the simply supported reinforced concrete beam, the employment of the Lagrange multiplier method that belongs to the class of stochastic quasigradient methods is demonstrated. The classical optimum design goal to minimise structural cost or weight under the constraint on the structural failure probability is taken for consideration. Optimisation problems solved with the Lagrangemultiplier method are formulated in form of general stochastic programming prob

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Ardiansyah, Sri Handayaningsih, and Deva Fathurrizki. "Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 3 (2025): 671–80. https://doi.org/10.25126/jtiik.2025128901.

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COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GW

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Ardiansyah, Sri Handayaningsih, and Deva Fathurrizki. "Grey Wolf Optimizer Termodifikasi Menggunakan Chaotic Uniform Initialization Untuk Estimasi Effort Cocomo." Jurnal Teknologi Informasi dan Ilmu Komputer 12, no. 3 (2025): 671–80. https://doi.org/10.25126/jtiik.20258901.

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COCOMO merupakan metode estimasi effort perangkat lunak berbasis parametrik yang banyak digunakan dan fleksibel diimplementasikan pada organisasi skala kecil hingga besar. Akan tetapi, kedua parameter COCOMO, yaitu multiplikatif dan eksponensial kerap memberikan hasil yang kurang presisi serta tidak realistis untuk diterapkan pada lingkungan pengembangan perangkat lunak saat ini. Untuk mengatasi masalah tersebut, beberapa penelitian mengusulkan pendekatan berbasis pencarian untuk mendapatkan nilai parameter yang tepat dengan menggunakan algoritma optimasi metaheuristik. Grey Wolf Optimizer (GW

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Rao, Singiresu S., and Ying Xiong. "A Hybrid Genetic Algorithm for Mixed-Discrete Design Optimization." Journal of Mechanical Design 127, no. 6 (2004): 1100–1112. http://dx.doi.org/10.1115/1.1876436.

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A new hybrid genetic algorithm is presented for the solution of mixed-discrete nonlinear design optimization. In this approach, the genetic algorithm (GA) is used mainly to determine the optimal feasible region that contains the global optimum point, and the hybrid negative subgradient method integrated with discrete one-dimensional search is subsequently used to replace the GA to find the final optimum solution. The hybrid genetic algorithm, combining the advantages of random search and deterministic search methods, can improve the convergence speed and computational efficiency compared with

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Raska, Pavel, and Ulrych Zdenek. "Simulation Optimization – Testing Selected Optimization Methods and their Setting of the Parameters." Advanced Materials Research 980 (June 2014): 198–202. http://dx.doi.org/10.4028/www.scientific.net/amr.980.198.

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The paper deals with testing optimization methods and their setting of the parameters used to search for the global optimum of specified objective functions. The objective functions were specified considering the objectives of the discrete event simulation models. We specified the evaluation methods considering the success of finding the global optimum (or the best found objective function value) the in defined search space. We tested Random Search, Hill Climbing, Tabu Search, Local Search, Downhill Simplex, Simulated Annealing, Differential Evolution and Evolution Strategy. After the testing

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Florea, Adrian-Catalin, and Razvan Andonie. "Weighted Random Search for Hyperparameter Optimization." International Journal of Computers Communications & Control 14, no. 2 (2019): 154–69. http://dx.doi.org/10.15837/ijccc.2019.2.3514.

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We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical

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MORAWSKI, THOMAS B., COLIN G. DRURY, and MARK H. KARWAN. "THE OPTIMUM SPEED OF VISUAL INSPECTION USING A RANDOM SEARCH STRATEGY." IIE Transactions 24, no. 5 (1992): 122–33. http://dx.doi.org/10.1080/07408179208964252.

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Uray, Esra, Serdar Carbas, Zong Woo Geem, and Sanghun Kim. "Parameters Optimization of Taguchi Method Integrated Hybrid Harmony Search Algorithm for Engineering Design Problems." Mathematics 10, no. 3 (2022): 327. http://dx.doi.org/10.3390/math10030327.

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Performance of convergence to the optimum value is not completely a known process due to characteristics of the considered design problem and floating values of optimization algorithm control parameters. However, increasing robustness and effectiveness of an optimization algorithm may be possible statistically by estimating proper algorithm parameters values. Not only the algorithm which utilizes these estimated-proper algorithm parameter values may enable to find the best fitness in a shorter time, but also it may supply the optimum searching process with a pragmatical manner. This study focu

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Tong, Bruce Kwong-Bun, Chi Wan Sung, and Wing Shing Wong. "Random Orthogonal Search with Triangular and Quadratic Distributions (TROS and QROS): Parameterless Algorithms for Global Optimization." Applied Sciences 13, no. 3 (2023): 1391. http://dx.doi.org/10.3390/app13031391.

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In this paper, the behavior and performance of Pure Random Orthogonal Search (PROS), a parameter-free evolutionary algorithm (EA) that outperforms many existing EAs on the well-known benchmark functions with finite-time budget, are analyzed. The sufficient conditions to converge to the global optimum are also determined. In addition, we propose two modifications to PROS, namely Triangular-Distributed Random Orthogonal Search (TROS) and Quadratic-Distributed Random Orthogonal Search (QROS). With our local search mechanism, both modified algorithms improve the convergence rates and the errors of

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Miftakhov, Eldar N. "Algorithm for Searching the Optimal Regulator Supply Mode in the Process of Manufacturing Polymer Products." Engineering technologies and systems 34, no. 4 (2024): 597–614. https://doi.org/10.15507/2658-4123.034.202404.597-614.

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Introduction. The high demand for polymer products creates the need for constant modernization of the technological aspects of their production, increasing the efficiency of which is impossible without a model description and solving problems of optimization of its main technological stages. The current needs for manufacturing the products with a specified structure and properties make the issue of creating tools for solving optimization problems very relevant. One of the tools for controlling the product molecular weight is using the fractional mode to supply a regulator, the composition and

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Na, Seung-Soo, and Dale G. Karr. "Product-Oriented Optimum Structural Design of Double-Hull Oil Tankers." Journal of Ship Production 18, no. 04 (2002): 237–48. http://dx.doi.org/10.5957/jsp.2002.18.4.237.

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A convenient interactive structural design system (ISSMID), which consists of a structural arrangement module, a scantling module, and an interface module, has been developed for the optimum design of double-hull oil tankers. The structural arrangement module defines the configuration and properties of the longitudinal, transverse, and transverse bulkhead members. The scantling module determines the scantling of the longitudinal, transverse, and transverse bulkhead members to obtain the minimum structural weight and fabrication cost. The interface module generates the data for the structural a

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Zhao, Hongmei, Heming Yao, Yuzhao Jiao, Taishan Lou, and Yunfei Wang. "An Improved Beetle Antennae Search Algorithm Based on Inertia Weight and Attenuation Factor." Mathematical Problems in Engineering 2022 (July 11, 2022): 1–20. http://dx.doi.org/10.1155/2022/7391145.

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The beetle antennae search algorithm is an effective bio-inspired algorithm. However, the algorithm is easy to fall into local optimal solution when dealing with high-dimensional and multimodal problems. An improved beetle antennae search algorithm based on inertia weight and attenuation factor was proposed in order to solve the problems. The inertia weights of normal distribution, versoria distribution, and random distribution are introduced into the weight to improve the search strategy, which was introduced to control the proportion of global search and local search so that the algorithm wi

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Liu, Guangzhong, Sheng Zhang, Guojie Ma, and Yipeng Pan. "Path Planning of Unmanned Surface Vehicle Based on Improved Sparrow Search Algorithm." Journal of Marine Science and Engineering 11, no. 12 (2023): 2292. http://dx.doi.org/10.3390/jmse11122292.

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In order to solve the problem of many constraints and a complex navigation environment in the path planning of unmanned surface vehicles (USV), an improved sparrow search algorithm combining cubic chaotic map and Gaussian random walk strategy was proposed to plan it. Firstly, in the population initialisation stage, cubic chaotic map was used to replace the random generation method of the traditional sparrow search algorithm to optimise the uneven initial distribution of the population and improve the global search ability of the population. Secondly, in the late iteration of the algorithm, the

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Peck, Charles C., and Atam P. Dhawan. "Genetic Algorithms as Global Random Search Methods: An Alternative Perspective." Evolutionary Computation 3, no. 1 (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

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Rabiatul Adawiyah. "Cluster Text Random Opinion Tweet In Yogyakarta Using Automatic Clustering." Jurnal Penelitian Rumpun Ilmu Teknik 2, no. 1 (2023): 73–89. http://dx.doi.org/10.55606/juprit.v2i1.1194.

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Tweet Besides making computations difficult, the data obtained is also inefficient and complicated to interpret. Therefore, it is necessary to explore how to overcome these problems. This study proposes an approach to find the global optimum and make automatic grouping by analyzing moving averages, namely K-Means Automatic Clustering. So the purpose of this study was to explore and evaluate high-dimensional data from a collection of tweets, namely random opinion text tweets in Yogyakarta. The K-means Automatic Clustering algorithm is used for clusters based on the data attributes that have bee

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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 (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 thro

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Al-Juboori, Muqdad, and Bithin Datta. "Optimum design of hydraulic water retaining structures incorporating uncertainty in estimating heterogeneous hydraulic conductivity utilizing stochastic ensemble surrogate models within a multi-objective multi-realisation optimisation model." Journal of Computational Design and Engineering 6, no. 3 (2018): 296–315. http://dx.doi.org/10.1016/j.jcde.2018.12.003.

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Abstract In order to find optimum and reliable designs for hydraulic water retaining structures (HWRSs), a reliability based optimum design (RBOD) model was used to quantify uncertainty in estimates of seepage characteristics due to uncertainty in heterogeneous hydraulic conductivity (HHC). This included incorporating reliability measures into minimum-cost HWRS designs and utilising a multi-realisation optimisation technique based on various stochastic ensemble surrogate models. To improve the efficiency of the RBOD model and the direct search optimisation solver, a multi-objective multi-reali

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da Silva, Rodrigo Ferreira, Lars Magnus Hvattum, and Fred Glover. "Combining solutions of the optimum satisfiability problem using evolutionary tunneling." MENDEL 26, no. 1 (2020): 23–29. http://dx.doi.org/10.13164/mendel.2020.1.023.

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The optimum satisfiability problem involves determining values for Boolean variables to satisfy a Boolean expression, while maximizing the sum of coefficients associated with the variables chosen to be true. Existing literature has identified a tabu search heuristic as the best method to deal with hard instances of the problem. This paper combines the tabu search with a simple evolutionary heuristic based on the idea of tunneling between local optima. When combining a set of solutions, variables with common values in all solutions are identified and fixed. The remaining free variables in the p

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Hao, Jian, Xu Yang, Chen Wang, Rang Tu, and Tao Zhang. "An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy." Applied Sciences 12, no. 22 (2022): 11573. http://dx.doi.org/10.3390/app122211573.

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Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random

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Dobrow, Robert P., and James Allen Fill. "Multiway Trees of Maximum and Minimum Probability under the Random Permutation Model." Combinatorics, Probability and Computing 5, no. 4 (1996): 351–71. http://dx.doi.org/10.1017/s096354830000211x.

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Multiway trees, also known as m–ary search trees, are data structures generalising binary search trees. A common probability model for analysing the behaviour of these structures is the random permutation model. The probability mass function Q on the set of m–ary search trees under the random permutation model is the distribution induced by sequentially inserting the records of a uniformly random permutation into an initially empty m–ary search tree. We study some basic properties of the functional Q, which serves as a measure of the ‘shape’ of the tree. In particular, we determine exact and a

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Niklas, Karol. "Search for optimum geometry of selected steel sandwich panel joints." Polish Maritime Research 15, no. 2 (2008): 26–31. http://dx.doi.org/10.2478/v10012-007-0061-6.

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Search for optimum geometry of selected steel sandwich panel joints Application of steel sandwich panels to ship structures requires many problems to be solved. Joints between the panels as well as those between the panels and other structures is one of the more difficult problems associated with the structures in question. This paper presents the searching for process of optimum geometry of a panel-to-panel joint of longitudinal arrangement, performed by means of the ANSYS software. A configuration was searched for of parameters which can ensure as-low-as possible values of geometrical stress

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Filatov, Heorhii V. "Optimum Design of Reinforced Cylindrical Shells Under Combined Axial Compression and Internal Pressure." Journal of Mechanical Engineering 24, no. 2 (2021): 50–58. http://dx.doi.org/10.15407/pmach2021.02.050.

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This paper discusses the use of the random search method for the optimal design of single-layered rib-reinforced cylindrical shells under combined axial compression and internal pressure with account taken of the elastic-plastic material behavior. The optimality criterion is the minimum shell volume. The search area for the optimal solution in the space of the parameters being optimized is limited by the strength and stability conditions of the shell. When assessing stability, the discrete rib arrangement is taken into account. In addition to the strength and stability conditions of the shell,

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