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Enhanced Grey Wolf Optimization Algorithm for Global Optimization

Joshi H., Arora S

Fundamenta Informaticae

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2017

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Vol. 153, nr 3

235--264

EN

Grey Wolf Optimizer (GWO) is a new meta-heuristic search algorithm inspired by the social behavior of leadership and the hunting mechanism of grey wolves. GWO algorithm is prominent in terms of finding the optimal solution without getting trapped in premature convergence. In the original GWO, half of the iterations are dedicated to exploration and the other half are devoted to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, an Enhanced Grey Wolf Optimization (EGWO) algorithm with a better hunting mechanism is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm and hence promising candidate solutions are generated. To verify the performance of our proposed EGWO algorithm, it is benchmarked on twenty-five benchmark functions with diverse complexities. It is then employed on range based node localization problem in wireless sensor network to demonstrate its applicability. The simulation results indicate that the proposed algorithm is able to provide superior results in comparison with some wellknown algorithms. The results of the node localization problem indicate the effectiveness of the proposed algorithm in solving real world problems with unknown search spaces.

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ABC-CAG: Covering Array Generator for Pair-wise Testing Using Artificial Bee Colony Algorithm

Bansal P., Sabharwal S., Mittal N., Arora S.

e-Informatica Software Engineering Journal

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2016

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Vol. 10, nr 1

9--29

EN

Testing is an indispensable part of the software development life cycle. It is performed to improve the performance, quality and reliability of the software. Various types of testing such as functional testing and structural testing are performed on software to uncover the faults caused by an incorrect code, interaction of input parameters, etc. One of the major factors in deciding the quality of testing is the design of relevant test cases which is crucial for the success of testing. In this paper we concentrate on generating test cases to uncover faults caused by the interaction of input parameters. It is advisable to perform thorough testing but the number of test cases grows exponentially with the increase in the number of input parameters, which makes exhaustive testing of interaction of input parameters imprudent. An alternative to exhaustive testing is combinatorial interaction testing (CIT) which requires that every t-way interaction of input parameters be covered by at least one test case. Here, we present a novel strategy ABC-CAG (Artificial Bee Colony-Covering Array Generator) based on the Artificial Bee Colony (ABC) algorithm to generate covering an array and a mixed covering array for pair-wise testing. The proposed ABC-CAG strategy is implemented in a tool and experiments are conducted on various benchmark problems to evaluate the efficacy of the proposed approach. Experimental results show that ABC-CAG generates better/comparable results as compared to the existing state-of-the-art algorithms.

3

Construction of Variable Strength Covering Array for Combinatorial Testing Using a Greedy Approach to Genetic Algorithm

Bansal P, Sabharwal S, Mittal N, Arora S

e-Informatica Software Engineering Journal

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2015

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Vol. 9, nr 1

87--105

EN

The limitation of time and budget usually prohibits exhaustive testing of interactions between components in a component based software system. Combinatorial testing is a software testing technique that can be used to detect faults in a component based software system caused by the interactions of components in an effective and efficient way. Most of the research in the field of combinatorial testing till now has focused on the construction of optimal covering array (CA) of fixed strength t which covers all t-way interactions among components. The size of CA increases with the increase in strength of testing t, which further increases the cost of testing. However, not all components require higher strength interaction testing. Hence, in a system with k components a technique is required to construct CA of fixed strength t which covers all t-way interactions among k components and all ti-way (where ti > t) interactions between a subset of k components. This is achieved using the variable strength covering array (VSCA). In this paper we propose a greedy based genetic algorithm (GA) to generate optimal VSCA. Experiments are conducted on several benchmark configurations to evaluate the effectiveness of the proposed approach.

4

Physico-Chemical Studies of Polyurethanes with Dipolar Chromophore

Arora S., Kumar S.

Polish Journal of Chemistry

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2007

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Vol. 81, nr 2

225-235

EN

Polyurethanes with varying concentrations of dipolar chromophore, namely 4-{4-[bis(2- hydroxyethyl) amino] phenylazo} benzonitrile were synthesized and characterized by thermal and spectral techniques. The effect of varying concentrations of dipolar chromophore on physicochemical properties of the polymers was studied. The polymers were obtained with very high yield possessing solubility in polar aprotic solvents like N,N-dimethylformamide, tetrahydrofuran, etc. Molecular weights of the resulting polymers determined by GPC were found to be in the range Mw = 5050–5730 (Mw/Mn =1.89–1.98). DSC curves of the polymers showed absence of melting process, indicating the polyurethanes to be noncrystalline. The synthesized polymers were found to possess high glass transition temperatures (Tg) and high thermal stabilities. Optical band gaps of these polyurethanes were in the range 2.30–2.51 eV. Vacuum-deposited thin films of these polyurethanes were corona-poled to study relaxation behaviour of aligned dipoles. The results indicate that polymeric films possess good temporal stability of aligned dipoles.