scholarly journals Application of Improved Sparrow Search Algorithm in Concrete

2021 ◽  
Vol 2082 (1) ◽  
pp. 012014
Author(s):  
Chengtian Ouyang ◽  
Feng Tang ◽  
Donglin Zhu ◽  
Yaxian Qiu ◽  
Yujia Liu
Keyword(s):  
Local Search ◽  
High Stability ◽  
Search Algorithm ◽  
Concrete Strength ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Strength Prediction

Abstract Compared with other algorithms, the performance of sparrow algorithm is better, but it also has shortcomings such as insufficient convergence and large randomness. For this reason, this paper proposes an improved sparrow search algorithm, which uses K-means to initialize the population to reduce the influence of randomness. Use sine and cosine search to improve the quality of the position of followers, and finally use adaptive local search to update the optimal solution, and apply it to concrete strength prediction. The results show that various improved sparrow search algorithms have certain advantages and high stability.

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

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.

scholarly journals Finding A Small Vertex Cover in Massive Sparse Graphs: Construct, Local Search, and Preprocess

2017 ◽  
Vol 59 ◽  
pp. 463-494 ◽  
Author(s):  
Shaowei Cai ◽  
Jinkun Lin ◽  
Chuan Luo
Keyword(s):  
Local Search ◽  
Real World ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Vertex Cover ◽  
Search Algorithms ◽  
Theory And Practice ◽  
Sparse Graphs ◽  
Massive Graphs

The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard combinatorial optimization problem of great importance in theory and practice. Due to its NP-hardness, there has been much interest in developing heuristic algorithms for finding a small vertex cover in reasonable time. Previously, heuristic algorithms for MinVC have focused on solving graphs of relatively small size, and they are not suitable for solving massive graphs as they usually have high-complexity heuristics. This paper explores techniques for solving MinVC in very large scale real-world graphs, including a construction algorithm, a local search algorithm and a preprocessing algorithm. Both the construction and search algorithms are based on low-complexity heuristics, and we combine them to develop a heuristic algorithm for MinVC called FastVC. Experimental results on a broad range of real-world massive graphs show that, our algorithms are very fast and have better performance than previous heuristic algorithms for MinVC. We also develop a preprocessing algorithm to simplify graphs for MinVC algorithms. By applying the preprocessing algorithm to local search algorithms, we obtain two efficient MinVC solvers called NuMVC2+p and FastVC2+p, which show further improvement on the massive graphs.

Restoration of Motion Blurred Image Based on PSO Combine Wiener Filter in Ship Imaging System

2014 ◽  
Vol 1006-1007 ◽  
pp. 739-742
Author(s):  
Hui Xuan Fu ◽  
Yu Chao Wang ◽  
Xun Su
Keyword(s):  
Imaging System ◽  
Search Algorithm ◽  
Wiener Filter ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Improve Image Quality ◽  
Blurred Image ◽  
Blurred Images ◽  
Ringing Effect

Ship internal equipment vibration will cause the imaging system platform vibration, resulting in blurred images. Wiener Filter is often used to restore the motion blurred image. The principle of the method expects to minimize the mean square error between the restore image and original image. However, this method has some constrains, if parameter selection improper, it generates ringing effect easily. Usually, most users select parameter by rule of thumb, so they frequently fail to generate the optimal solution. In order to get high quality restore image, eliminate the ringing effect, a new approach based on particle swarm optimization (PSO) Wiener Filter was proposed, which automatically adjusts the parameter for Wiener Filter, this method seek the optimal solution by transferring information between individuals and information sharing, which is a highly efficient parallel search algorithm, insuring the accuracy of parameter selection, effectively reducing the ringing effect after image restoration, improve image quality of restoration.

scholarly journals An Improved Iterated Local Search Algorithm for the Static Partial Repositioning Problem in Bike-Sharing System

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiong Tang ◽  
Zhuo Fu ◽  
Dezhi Zhang ◽  
Meng Qiu ◽  
Minyi Li
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Target Number ◽  
Absolute Deviation ◽  
Penalty Cost ◽  
Bike Sharing ◽  
Single Vehicle ◽  
Loading And Unloading

In this paper, a single-vehicle static partial repositioning problem (SPRP) is investigated, which distinguishes the user dissatisfaction generated by different stations. The overall objective of the SPRP is to minimize the weighted sum of the total operational time and the total absolute deviation from the target number of bikes at all stations. An iterated local search is developed to solve this problem. A novel loading and unloading quantity adjustment operator is proposed to further improve the quality of the solution. Experiments are conducted on a set of instances from 30 to 300 stations to demonstrate the effectiveness of the proposed customized solution algorithm as well as the adjustment operator. Using a small example, this paper also reveals that the unit penalty cost has an effect on the repositioning strategies.

scholarly journals Composite Differential Search Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Bo Liu
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Control Parameters ◽  
Original Algorithm ◽  
New Space ◽  
Random Method ◽  
Better Than

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.

A Parallel Local Search Algorithm for Clustering Large Biological Networks

2017 ◽  
Vol 27 (03n04) ◽  
pp. 1750007
Author(s):  
Gaetano Coccimiglio ◽  
Salimur Choudhury
Keyword(s):  
Local Search ◽  
Biological Networks ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Local Search Algorithm ◽  
Effective Technique ◽  
User Input ◽  
Time Period ◽  
Cluster Quality

Clustering is an effective technique that can be used to analyze and extract useful information from large biological networks. Popular clustering solutions often require user input for several algorithm options that can seem very arbitrary without experimentation. These algorithms can provide good results in a reasonable time period but they are not above improvements. We present a local search based clustering algorithm free of such required input that can be used to improve the cluster quality of a set of given clusters taken from any existing algorithm or clusters produced via any arbitrary assignment. We implement this local search using a modern GPU based approach to allow for efficient runtime. The proposed algorithm shows promising results for improving the quality of clusters. With already high quality input clusters we can achieve cluster rating improvements upto to 33%.

scholarly journals A Variable Neighborhood Walksat-Based Algorithm for MAX-SAT Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Noureddine Bouhmala
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Boolean Formula ◽  
Local Search Algorithms ◽  
Neighborhood Structure ◽  
Np Complete ◽  
Max Sat ◽  
Almost All

The simplicity of the maximum satisfiability problem (MAX-SAT) combined with its applicability in many areas of artificial intelligence and computing science made it one of the fundamental optimization problems. This NP-complete problem refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weights of satisfied clauses) in a Boolean formula. The Walksat algorithm is considered to be the main skeleton underlying almost all local search algorithms for MAX-SAT. Most local search algorithms including Walksat rely on the 1-flip neighborhood structure. This paper introduces a variable neighborhood walksat-based algorithm. The neighborhood structure can be combined easily using any local search algorithm. Its effectiveness is compared with existing algorithms using 1-flip neighborhood structure and solvers such as CCLS and Optimax from the eighth MAX-SAT evaluation.

scholarly journals NEIGHBORHOOD STRUCTURES FOR GPU-BASED LOCAL SEARCH ALGORITHMS

2010 ◽  
Vol 20 (04) ◽  
pp. 307-324 ◽  
Author(s):  
THÉ VAN LUONG ◽  
NOUREDINE MELAB ◽  
EL-GHAZALI TALBI
Keyword(s):  
Local Search ◽  
Gpu Computing ◽  
Search Space ◽  
Search Algorithms ◽  
Local Search Algorithms ◽  
Computation Process ◽  
Neighborhood Structures ◽  
Speed Up ◽  
Hard Problems

Local search algorithms are powerful heuristics for solving computationally hard problems in science and industry. In these methods, designing neighborhood operators to explore large promising regions of the search space may improve the quality of the obtained solutions at the expense of a high-cost computation process. As a consequence, the use of GPU computing provides an efficient way to speed up the search. However, designing applications on a GPU is still complex and many issues have to be faced. We provide a methodology to design and implement different neighborhood structures for LS algorithms on a GPU. The work has been evaluated for binary problems and the obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.

scholarly journals Simulated Annealing with Exploratory Sensing for Global Optimization

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Majid Almarashi ◽  
Wael Deabes ◽  
Hesham H. Amin ◽  
Abdel-Rahman Hedar
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Initial Temperature ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Algorithms ◽  
Two Phases ◽  
Number Of Iterations

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.

Diagnostic Strategy Optimization Based on Predatory Search Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 2441-2444
Author(s):  
Jin Luo ◽  
Qi Bin Deng ◽  
Chen Meng
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Global Search ◽  
State Probability ◽  
Diagnostic Strategy ◽  
Local Optimal Solution ◽  
Predatory Search Algorithm

With respect to the inherent NP-hard complexity of Optimization of testability diagnostic strategy problem, a predatory search algorithm simulating animal predatory strategies was designed. This algorithm adopted the gross test expense including state probability, isolation matrix and test expense as its objective function, defined local and global search by the restriction value of search space based on two points exchange, and realized the conversion between local and global search by adjusting the restriction value of search space. It had better ability to conduct local search and jump out of local optimal solution simultaneously, and provided a better resolution for the optimization of testability diagnostic strategy.

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