Krishn K. Mishra

An approach for mutation testing using elitist genetic algorithm

Mutation Testing is used as fault-based testing to overcome limitations of other testing approaches but it is recognized as expensive process. In mutation testing, a good test case is one that kills one or more mutants, by producing different mutant output from the original program. Evolutionary algorithms have been proved its suitability for reducing the cost of data generation in different testing methodologies. In order to reduce the cost of mutation testing, efficient test cases are generated that reveal faults and kill mutants. In this paper, we develop a new strategy for generating efficient test input data in the context of mutation testing.

Dynamic-PSO: An improved particle swarm Optimizer

In this paper, a variant of particle swarm optimization (PSO) is presented to handle the problem of stagnation encounters in PSO which may lead to get it trapped in local optima and premature convergence particularly in multimodal problems. The proposed scheme Dynamic-PSO (DPSO) does not disturb the fast convergence characteristics of PSO by keeping the basic concept of PSO unaffected. When particles personal best and swarms global best position do not improve in successive generation i.e. start stagnating DPSO provides dynamicity to particles externally in such a manner that stagnated particles move towards potentially better unexplored region to maintain diversity as this increases chance to recover from stagnation. By identifying and curing stagnated particles, it also avoids the problems of getting trapped in local optima and premature convergence. We have compared the proposed algorithm DPSO with basic PSO and its widely accepted variants over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2013). Results show that the proposed variant performs better in comparison with other peer algorithms.

Notice of Retraction Generation of efficient test data using path selection strategy with elitist GA in regression testing

Regression testing is an expensive and frequently executed maintenance process used to revalidate modified software. Various problems are associated with regression testing such as regression test selection problem, coverage identification problem, test case execution problem, test case maintenance problem etc. In test selection problem, appropriate and effective test data is to be selected from the input domain of test data. One more problem may arise, when tester has to select the modified paths from the set of modified path for test case execution i.e. path selection problem. To overcome these problems, this paper presents a combined approach by which the stated problems are resolved in effective manner. By this approach, tester can identify the appropriate paths for test case execution and also generate efficient test data using elitist version of GA. The proposed approach enables tester to execute the test cases in order to increase their effectiveness to find faults taking minimum efforts. This approach is used in regression testing to choose an appropriate subset of test cases by using elitist GA, among a previously run test suite for a software system, based on the information about the modifications made to the system for enhancement.

Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking

A variant of Multi-Objective Particle Swarm Optimization (MOPSO), named as MOPSOtridist, is proposed in this paper. To improve the performance of existing MOPSO algorithms, new leader selection strategy and personal best (pbest) replacement scheme is introduced in this variant. In existing MOPSO algorithms, selection of leader is done only on the basis of particle’s current position and particle movement history is not taken into account. In MOPSOtridist, this information is used by selecting the most appropriate leader from the archive which has minimum distance from the region where the particle had visited recently. The proposed leader selection strategy efficiently explores the whole Pareto front by attracting the distinct regions explored by different particles. Additionally, a pbest replacement scheme is introduced to handle its stagnation at local optimal solutions by replacing the stagnated pbest of the particle with a new archive member, which is at maximum distance from the particle’s local optimal solutions. This will add diversity and forces those particles to explore other regions. For measuring the distance between particle’s regions and archive member, triangular distance (tridist) is used. The proposed MOPSOtridist algorithm along with other widely known variants of MOPSO, are tested exhaustively over two series of benchmark functions ZDT and DTLZ. The experiment results show that the proposed algorithm outperforms other MOPSO algorithms significantly in terms of standard performance metrics. Further, the proposed variant MOPSOtridist is applied to digital image watermarking problem for colour images in RGB colour space. Results demonstrate that MOPSOtridist efficiently produce optimal values of watermark strength to achieve good trade-offs between imperceptibility and robustness objectives.

Incorporating logic in Artificial Bee Colony (ABC) algorithm to solve first order logic problems: The logical ABC

The fascination for generating reasons and drawing inferences has given a tremendous impetus to research in theoretical computer science. In spite of having well defined constructs and globally accepted notations for logic and First-order theorem provers, theorem proving is still a semi-decidable problem having exponential time complexity. On the other hand swarm intelligence is a swiftly growing research area for solving optimization problems. This paper presents a novel approach for automated theorem proving using meta-heuristics. In the present research we have tried to combine these two entirely different zones of computer science, i.e. meta-heuristics and concrete logic via modeling theorem provers as an optimization problem in a sound practical manner. Also we have experimentally shown how to automate first order reasoning using Artificial Bee Colony algorithm on a sample problem expressed in First-order predicate calculus.

Optimizing the reliability of communication network using specially designed genetic algorithm

All areas relating to telecommunications, electricity distribution, and gas pipeline require Topological optimization. It also has a major importance in the computer communication industry, when considering network reliability. In this paper, we have used GA with specialized encoding, initialization, local search operators with specially designed crossover operator called alternating crossover [21] to optimize the design of communication network topologies, as this NP-hard problem is often highly constrained so random initialization and standard genetic operators usually generate infeasible networks.

A variant of NSGA-II for solving priority based optimization problems

Many real life problems require optimization of more than one objective functions, these problems are known as multi-objective optimization problems. Although many algorithms are available in literature to solve these types of problems [2, 3, 4, 5, 21] but they treat every objective function equally. Sometimes, on the basis of problem to be optimized, different objective functions can be assigned different priority. In this way, we can reduce the burden of searching in all objective directions, those having higher priority will contribute in search space, those having low priority will be selected by local search from the assigned search space. For dealing such type of problems a modified version of NSGA-II is presented in the paper. This modified algorithm is used to solve a multi-objective problem, which is related to Rotary furnace that is used in small-scale foundry.

An Environmental Adaption Method with real parameter encoding for dynamic environment

A dynamic version of Environmental Adaption Method (EAM) is proposed in this paper. Environmental Adaption Method for Dynamic Environment (EAMD) is an improvement over EAM, which works in dynamic environment with real valued parameters. Unlike EAM the theory of this algorithm is based on adaption of species in dynamic environment which gradually becomes more verse and deadly for their denizens. The species which are able to adapt in the changing environment, improves their fitness value by enhancing their phenotypic structure in the upcoming generations. Sudden and gradual dynamic changes in the environment assist species to converge towards the optimal fitness. Unlike EAM, EAMD is suitable for both unimodal and multimodal problems without the need of an alteration operator as there is enough diversity since the adaption is randomized, i.e. each possible solution can adapt anywhere within the search space. EAMD is compared with various algorithms tested on 24 benchmark functions against the Black Box Optimization Benchmarking (BBOB) test-bed at different dimensions with very promising results and EAMD shows its superiority over other state-of-the-art algorithms.

Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications

Abstract Particle Swarm Optimization (PSO) algorithms often face premature convergence problem, specially in multimodal problems as it may get stuck in specific point. In this paper, we have enhanced Dynamic-PSO i.e. and an extention of our earlier research work. This newly proposed algorithm Guided Dynamic-PSO (GDPSO) also targets the particles whose personal best get stuck i.e. their personal best does not improve for fixed number of iterations similar to DPSO, however a new approach is proposed for replacing personal bests of such particles. The replacement of this new personal best is done on the basis of sharing fitness so that better diversity can be provided to avoid the problem. The performance of GDPSO has been compared with PSO and its variants including DPSO over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2015). Results show that the performance of GDPSO is better in comparison with other peer algorithms. Further effectiveness of GDPSO is demonstrated in digital image watermarking. Digital image watermarking schemes primarily focus on providing good tradeoff between imperceptibility and robustness along with reliability in watermarked images produced for wide variety of applications. To support watermarking scheme in achieving this tradeoff, suitable watermark strength is identified in the form of scaling factor using GDPSO for colored images. Results achieved through GDPSO are compared with PSO and other widely accepted variants of PSO over different combination of host and watermark images. Experiment results demonstrate that performance of underline watermarking scheme when used with GDPSO, in terms of imperceptibility and robustness, is better than other variants of PSO.

Variant of Differential Evolution Algorithm

Differential evolution is a nature-inspired optimization technique. It has achieved best solutions on large area of test suits. DE algorithm is efficient in programming and it has broad applicability in engineering. This paper presents modified mutation vector generation strategy of basic DE for solving stagnation problem. A new variant of differential evolution that is DE_New has been proposed and the performance of DE_New is tested on Comparing Continuous Optimisers (COCO) framework composed of 24 benchmark functions and found DE_New has better exploration capability inside the given search space in comparison to GA, DE-PSO, DE-AUTO on Black-Box Optimization Benchmarking (BBOB) 2015 devised by COCO.