An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO) is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment

A hybrid multiple attribute decision making method for solving problems of industrial environment

The selection of appropriate alternative in the industrial environment is an important but, at the same time, a complex and difficult problem because of the availability of a wide range of alternatives and similarity among them. Therefore, there is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations. In this paper, a hybrid decision making method of graph theory and matrix approach (GTMA) and analytical hierarchy process (AHP) is proposed. Three examples are presented to illustrate the potential of the proposed GTMA-AHP method and the results are compared with the results obtained using other decision making methods

PERIODATE OXIDATION OF PEG–600, AN ESSENTIAL PHARMACEUTICAL POLYMER

Objective: To study the kinetics of periodate oxidation of polyethylene glycol-600 (PEG-600), a familiar non-toxic polymer used in pharmaceutical and other fields of industry. Methods: Reactions were carried out in alkaline medium and measured the kinetics by iodometry. One oxygen atom loss or two electrons transfer was observed per each molecule of periodate i.e., the rate of reaction was measured periodate converts to iodate because the formed iodate species is unable to oxidize the substrate molecules. Results: Based on log (a-x) versus t plots, order w. r. t. oxidant (periodate) is unity. Reactions were found to be independent of substrate (PEG-600) concentration. A decrease in rate with an increase in alkali concentration [OH–] was found and order was inverse fractional. Temperature dependence of reaction rate was studied and then calculated the corresponding Arrhenius parameters. Conclusion: An appropriate rate law was proposed by considering the above experimental results

Quenching of fluorescence of aromatic molecules by graphene due to electron transfer

Investigations on the fluorescence quenching of graphene have been carried out with two organic donor molecules, pyrene butanaoic acid succinimidyl ester (PyBS, I) and oligo(p-phenylenevinylene) methyl ester (OPV-ester, II). Absorption and photoluminescence spectra of I and II recorded in mixture with increasing the concentrations of graphene showed no change in the former, but remarkable quenching of fluorescence. The property of graphene to quench fluorescence of these aromatic molecules is shown to be associated with photo-induced electron transfer, on the basis of fluorescence decay and time-resolved transient absorption spectroscopic measurements.Comment: 18 pages, 6 figure

Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems

A simple yet powerful optimization algorithm is proposed in this paper for solving the constrained and unconstrained optimization problems. This algorithm is based on the concept that the solution obtained for a given problem should move towards the best solution and should avoid the worst solution. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. The performance of the proposed algorithm is investigated by implementing it on 24 constrained benchmark functions having different characteristics given in Congress on Evolutionary Computation (CEC 2006) and the performance is compared with that of other well-known optimization algorithms. The results have proved the better effectiveness of the proposed algorithm. Furthermore, the statistical analysis of the experimental work has been carried out by conducting the Friedman’s rank test and Holm-Sidak test. The proposed algorithm is found to secure first rank for the ‘best’ and ‘mean’ solutions in the Friedman’s rank test for all the 24 constrained benchmark problems. In addition, for solving the constrained benchmark problems, the algorithm is also investigated on 30 unconstrained benchmark problems taken from the literature and the performance of the algorithm is found better

A note on “An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems”

A paper published by Maniya and Bhatt (2011) (An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems, Computers & Industrial Engineering, 61, 542-549) proposed an alternative multiple attribute decision making method named as “Preference Selection Index (PSI) method” for selection of an optimal facility layout design. The authors had claimed that the method was logical and more appropriate and the method gives directly the optimal solution without assigning the relative importance between the facility layout design selection attributes. This note discusses the mathematical validity and the shortcomings of the PSI method