Smart Root Search (SRS): A New Search Algorithm to Investigate Combinatorial Problems

2015 ◽  
Author(s):  
Narjes Khatoon Naseri ◽  
Elankovan Sundararajan ◽  
Masri Ayob ◽  
Amin Jula
Keyword(s):  
Search Algorithm ◽  
Combinatorial Problems

scholarly journals A Tabu Search Algorithm for General Threshold Visual Cryptography Schemes

2021 ◽  
Vol 26 (3) ◽  
pp. 329-335
Author(s):  
Kanusu Srinivasa Rao ◽  
Mandapati Sridhar
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Visual Cryptography ◽  
Mathematical Optimization ◽  
Combinatorial Problems ◽  
Pixel Expansion ◽  
Performance Metric ◽  
Critical Issues ◽  
Original Message ◽  
Display Quality

In Visual Cryptography Schemes (VCSs), for message n transparencies are generated, such that the original message is visible if any k of them are stacked. VCS especially for large values of k and n, the pixel expansion’s reduction and enhancement of the recovered images’ display quality continue to be critical issues. In addition to this, it is challenging to develop a practical and systematic approach to threshold VCSs. An optimization-based pixel-expansion-free threshold VCSs approach has been proposed for binary secret images’ encryption. Along with contrast, blackness is also treated as a performance metric for assessing the recovered images’ display quality. An ideally secure technique for a secret image’s protection through its partition into shadow images (known as shadows) is the Visual Secret Sharing (VSS) scheme. Acquirement of a smaller shadow size or a higher contrast is the VSS schemes’ latest focus. The white pixels’ frequency has been utilized to demonstrate the recovered image’s contrast in this work. While the Probabilistic VSS (ProbVSS) scheme is non-expansible, it can also be readily deployed depending upon the traditional VSS scheme. Initially, this work has defined the problem as a mathematical optimization model such that, while contingent on blackness and density-balance constraints, there is the maximization of the recovered images’ contrast. Afterward, an algorithm dependent on the Tabu Search (TS) is devised in this work for this problem’s resolution. Multiple complicated combinatorial problems have been successfully resolved with the powerful TS algorithm. Moreover, this work has attempted to bolster the contrast through the density-balance constraint’s slight relaxation. Compared to the older techniques, the proposed optimization-based approach is superior regarding the recovered images’ display quality and the pixel expansion factor from the experimental outcomes.

scholarly journals Clustering-Based Binarization Methods Applied to the Crow Search Algorithm for 0/1 Combinatorial Problems

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1070 ◽  
Author(s):  
Sergio Valdivia ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Nicolás Caselli ◽  
Fernando Paredes ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Combinatorial Problems ◽  
Clustering Methods ◽  
Simple Task ◽  
Expert User ◽  
Real Domain ◽  
Internal Core ◽  
Problem Solvers ◽  
Clustering Problems

Metaheuristics are smart problem solvers devoted to tackling particularly large optimization problems. During the last 20 years, they have largely been used to solve different problems from the academic as well as from the real-world. However, most of them have originally been designed for operating over real domain variables, being necessary to tailor its internal core, for instance, to be effective in a binary space of solutions. Various works have demonstrated that this internal modification, known as binarization, is not a simple task, since the several existing binarization ways may lead to very different results. This of course forces the user to implement and analyze a large list of binarization schemas for reaching good results. In this paper, we explore two efficient clustering methods, namely KMeans and DBscan to alter a metaheuristic in order to improve it, and thus do not require on the knowledge of an expert user for identifying which binarization strategy works better during the run. Both techniques have widely been applied to solve clustering problems, allowing us to exploit useful information gathered during the search to efficiently control and improve the binarization process. We integrate those techniques to a recent metaheuristic called Crow Search, and we conduct experiments where KMeans and DBscan are contrasted to 32 different binarization methods. The results show that the proposed approaches outperform most of the binarization strategies for a large list of well-known optimization instances.

scholarly journals Analyzing Evolutionary Optimization in Noisy Environments

2018 ◽  
Vol 26 (1) ◽  
pp. 1-41 ◽  
Author(s):  
Chao Qian ◽  
Yang Yu ◽  
Zhi-Hua Zhou
Keyword(s):  
Minimum Spanning Tree ◽  
Search Algorithm ◽  
Combinatorial Problems ◽  
Maximum Matching ◽  
Noise Tolerance ◽  
Threshold Selection ◽  
Noisy Environments ◽  
Exact Evaluation ◽  
Negative Effect ◽  
The One

Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms (EAs), a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoretical analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better.

scholarly journals Feature Selection using Stochastic Diffusion Search Algorithm in Big Data Analysis

2020 ◽  
Vol 8 (4S5) ◽  
pp. 236-242
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Big Data Analysis ◽  
Combinatorial Problems ◽  
Support Vector ◽  
Feature Subset ◽  
Stochastic Diffusion ◽  
Stochastic Diffusion Search

Big Data analysis has been viewed as the processing or mining of massive amounts of data used to retrieve information which is useful from large datasets. Among all the methods employed to deal with the analysis of Big Data, the selection of a feature is found extremely effective. A common approach which includes search making use of feature-based subsets which is relevant to the topic, tends to represent the dataset with its actual description. However, a search that makes use of such a subset is a combinatorial problem which is time-consuming. All commonly used meta-heuristic algorithms to facilitate feature choice. The Stochastic Diffusion Search (SDS) based algorithm has been a multi-agent global search algorithm based on agent interaction is simple to overcome combinatorial problems. The SDS will choose the feature subset for the task of classification. The Classification and Regression Tree (CART), the Naïve Bayes (NB), the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN) have been used to improve the performance. Results proved that the proposed method was able to achieve a better performance than existing techniques.

scholarly journals Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems

2020 ◽  
Vol 4 (4) ◽  
pp. 664-671
Author(s):  
Gabriella Icasia ◽  
Raras Tyasnurita ◽  
Etria Sepwardhani Purba
Keyword(s):  
Tabu Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Combinatorial Problems ◽  
Hill Climbing ◽  
Tabu Search Algorithm ◽  
Scheduling Problems ◽  
Examination Timetabling ◽  
Timetabling Problem

Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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