An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

AbstractThe main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test

Global gbest guided-artificial bee colony algorithm for numerical function optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

Algorithms for possibility linguistic single-valued neutrosophic decision-making based on COPRAS and aggregation operators with new information measures

The objective of this work is to introduce the concept of the possibility linguistic single-valued neutrosophic set (PLSVNS) for better dealing with the imprecise and uncertain information during the decision-making process. The prominent characteristics of this set are that it considers two distinctive sorts of information such as the membership, indeterminacy, non-membership degrees, and their corresponding possibility degree. In it, first, we stated some operational laws, score and accuracy functions, comparison laws between the pairs of the set. Then, we define weighted averaging and geometric aggregation operators (AOs) to collaborate the PLSVNSs into a single one. Further, we present two algorithms based on a complex proportional assessment (COPRAS) method and AOs based method under PLSVNS information to solve the decision-making problems. In these methods, the information related to weights of decision makers and criteria is determined with the help of a distance and entropy measures. Finally, a practical real-life example is provided to expose the materialness and the viability of our work

New logarithmic operational laws and their applications to multiattribute decision making for single-valued neutrosophic numbers

Neutrosophic set, initiated by Smarandache, is a novel tool to deal with vagueness considering the truth, indeterminacy and falsity memberships satisfying the condition that their sum is less than 3. This set can be used to characterize the information more accurately than the intuitionistic fuzzy set. Under this set, the objective of this manuscript is to present some new operational laws called as logarithm operational laws with real number base k for the single-valued neutrosophic (SVN) numbers. Various desirable properties of the proposed operational laws are contemplated. Further, based on these laws, different weighted averaging and geometric aggregation operators are developed

Bibliometrics and scientometrics in India: An overview of studies during 1995-2014, Part I: Indian publication output and its citation impact

An analysis of 801 papers published in the area of bibliometrics and scientometrics during 1995-2014 indicates a steep increase in the number of papers published by Indian researchers as compared to the number of papers published during 1970-1994. This indicates a growing interest of Indian scholars in scientometrics and bibliometrics. The paper provides several reasons for this steep increase. The main focus of research is on bibliometric assessment of India and other countries followed by cross national assessment and bibliometric analysis of individual journals. CSIR-NISTADS is the top producing institute contributing about one-third (31.4%) of the total output followed by the output of Bhabha Atomic Research Centre and CSIR-NISCAIR. The distribution of citation data indicates that about one-fifth (21.7%) papers remained uncited. The paper identifies journals in which these uncited papers were published. Only 15% papers were cited more than 20 times. Most of the prolific authors as well as highly cited authors were from the institutions belonging to the Council of Scientific and Industrial Research. Among all authors B.M. Gupta (CSIR-NISTADS) produced the highest number of papers, but the impact as seen in terms of citation per paper and relative citation impact, S. Arunachalam (MSSRF) topped the list

Scientometrics of Indian crop science research as reflected by the coverage inScopus, CABI and ISA databases during 2008-2010

The paper analyses scientific output of India in the discipline of crop sciences as reflected by the coverage of scientificoutput in three different databases i.e. SCOPUS, CAB Abstracts and ISA (Indian Science Abstracts) during 2008-2010. Theanalysis indicates that highest number of papers was published on rice and wheat crop. Agricultural universities andinstitutions under the aegis of Indian Council of Agricultural Research (ICAR) were most productive institutions. Most ofthe papers were published in Indian journals with low impact factor. Environment and Ecology, Indian Journal ofAgricultural Sciences and Research on Crops were the most preferred journals used by the Indian scientists. The majorresearch is focused on ‘genetics and plant breeding’ followed by ‘soil, climate and environmental aspects’ and ‘agronomicaspects’. The authorship pattern reveals that co-authored papers accounted for 72% of total output

Bibliometrics and scientometrics in India: An overview of studies during 1995-2014Part II: Contents of the articles in terms of disciplines and their bibliometric aspects

This part of the study highlights the contents of the published articles in terms of various disciplines or sub-disciplines and the bibliometric aspects discussed in these articles. The analysis of 902 papers published by Indian scholars during1995-2014 indicates that the main focus of bibliometrics/scientometrics is on assessment of science and technology in India in different sub-disciplines including contributions by Indian states and other individual countries followed by bibliometric analysis of individual journals. Papers dealing with bibliometric laws received a low priority as compared to other subdisciplines of bibliometrics/scientometrics. The analysis of data indicates that the share of theoretical studies using mathematical and statistical techniques which were missing in the earlier period (1970-1994) has increased during 1995-2014. The field of medicine as a discipline received the highest attention as compared to other disciplines

A quick gbest guided artificial bee colony algorithm for stock market prices prediction

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values

Scientometrics of cereal crops research in India as reflected through Indian Science Abstracts and CAB Abstracts during 1965-2010

The paper analyses publication output of India on cereal crops as reflected by its coverage in Indian Science Abstracts (ISA) and CAB Abstracts during 1965-2010.The analysis indicates that highest number of papers (43.80%) was published on rice, followed by wheat (24.28%). Agricultural universities and institutions under aegis of Indian Council of Agricultural Research (ICAR) were most productive. Most of the papers were published in Indian journals with low impact factor. The highest number of papers was published in Indian Journal of Agricultural Sciences, followed by Indian Journal of Agronomy, Madras Agricultural Journaland Journal of Maharashtra Agricultural University. Indian Agricultural Research Institute, New Delhi,Tamil Nadu Agricultural University, Coimbatoreand Punjab Agricultural University, Ludhianacontributed about 7% of papers each. The major research was focused on ‘genetic and plant breeding’ (28.2%) followed by ‘agronomic aspects’ (27.9%) and pest, diseases and pest control (19.7%). The authorship pattern reveals that co-authored papers accounted for 90% of total output. Citation analysis of the study using Google scholar reveals that 57% of the papers remained uncited and 36.8% papersreceived citations ranging from 1 to 10.Highest number of citations were received by papers published in Indian Journal of Agronomy(1446), followed by Indian Journal of Agricultural Science (1211), Euphytica (1109) and Theoretical and Applied Genetics (1000