A Rough Sets based modified Scatter Search algorithm for solving 0-1 Knapsack problem

Rezazadeh, Hassan

doi:10.5267/j.dsl.2015.2.001

Cited by 7 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this method did not consider the actual disassembly data. SS has successfully used to solve the mixed-model assembly line sequencing and knapsack problems [109,[111][112][113][114][115][116].…”

Section: Image Processingmentioning

confidence: 99%

A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges

Kalra

Yadav

Kumar

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Recent years have witnessed the use of metaheuristic algorithms to solve the optimization problems that usually require extensive computations and time. Among others, scatter search is the widely used evolutionary metaheuristic algorithm. It uses the information of global optima, which is stored in a different and unique set of solutions. In this paper, an updated review of scatter search (SS) is given. SS has been successfully applied in a variety of research problems, namely, data mining, bioinformatics, and engineering design. This paper presents the modified and hybrid versions of SS with their applications. The control strategies are discussed to show their impact on the performance of SS. various issues and future directions related to SS are also discussed. It inspires innovative researchers to use this algorithm for their research domains.

show abstract

Section: Image Processingmentioning

confidence: 99%

A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges

Kalra

Yadav

Kumar

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In the first one with full certainty and by using knowledge B , the collection of objects is categorized as the members of the set X . In the same way, by using the knowledge that belongs to the complement of X , the collection of objects can be identified without any uncertainty (Rezazadeh, 2015).…”

Section: Rough and Grey Set Theorymentioning

confidence: 99%

Designing a medical rule model system by using rough–grey modeling

Karimi

Hojati

2020

View full text Add to dashboard Cite

PurposeIn this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.Design/methodology/approachSome tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.FindingsIn this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.Research limitations/implicationsThe total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.Originality/valueThe main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.

show abstract

“…The positive region or lower approximation of X is the collection of those objects that can be classified with full certainty as members of the set X, using knowledge B. Similarly, the negative region is the collection of objects with which it can be determined without any ambiguity, using knowledge that belongs to the compliment of X (Rezazadeh, 2015) Author…”

Section: Rough Set Theorymentioning

confidence: 99%

Designing an inference engine for assessment of researchers’ maturity using rough set theory

Karimi

Moghadam

Mardani

2018

View full text Add to dashboard Cite

Purpose This paper aims to design an expert system that gets data from researchers and determines their maturity level. This system can be used for determining researchers’ support programs as well as a tool for researchers in research-based organizations. Design/methodology/approach This study focuses on designing the inference engine as a component of an expert system. To do so, rough set theory is used to design rule models. Various complete, discretizing and reduction algorithms are used in this paper, and different models were run. Findings The proposed inference engine has the validity of 99.8 per cent, and the most important attributes to determine the maturity level of researchers in this model are “commitment to research” and “attention to research plan timeline”. Research limitations/implications To accurately determine researchers’ maturity model, solely referring to documents and self-reports may reduce the validation. More validation could be reached through using assessment centers for determining capabilities of samples and observations in each maturity level. Originality/value The assessment system for the professional maturity of researchers is an appropriate tool for funders to support researchers. This system helps the funders to rank, validate and direct researchers. Furthermore, it is a valid criterion for researchers to evaluate and improve their abilities. There is not any expert system to assess the researches in literature, and all models, frameworks and software are conceptual or self-assessment.

show abstract

A Rough Sets based modified Scatter Search algorithm for solving 0-1 Knapsack problem

Cited by 7 publications

References 39 publications

A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges

A Comprehensive Review on Scatter Search: Techniques, Applications, and Challenges

Designing a medical rule model system by using rough–grey modeling

Designing an inference engine for assessment of researchers’ maturity using rough set theory

Contact Info

Product

Resources

About