Jitendra Rajpurohit

Survey on Different Level of Audio Watermarking Techniques

Audio Watermarking is useful technique for audio systems. This technique can work on different domains like frequency and time. By using the different scheme of watermarking at different levels of audio, it can be secure from many types of attacks. This paper shows some techniques which can be used to secure the audio system from attacks and survey on various transformation techniques for embedding or extracting watermark.

Shuffled Frog Leaping Algorithm with Adaptive Exploration

Shuffled frog leaping algorithm is a nature inspired memetic stochastic search method which is gaining the focus of researchers since it was introduced. SFLA has the limitation that its convergence speed decreases towards the later stage of execution and it also tends to stuck into local extremes. To overcome such limitations, this paper first proposes a variant in which a few new random frogs are generated and the worst performing frogs population are replaced by them. Experimental results show that a high number of replaced frogs does not always provide better results. As the execution progresses the optimized number of replaced frogs decreases. Based on the experimental observations, the paper then proposes another variant in which the number of replaced frogs adapts to the stage of the execution and hence provides the best results regardless of the stage of execution. Experiments are carried out on five benchmark test functions.

Accelerated Shuffled Frog-Leaping Algorithm

Shuffled frog-leaping algorithm (SFLA) is a recent addition to the family of stochastic search methods that mimic the social and natural behavior of species. SFLA combines the advantages of local search process of particle swarm optimization (PSO) and mixing of information of the shuffled complex evolution. The basic idea behind modeling of such algorithms is to achieve near to global solutions to the large-scale optimization problems and complex problems which cannot be solved using deterministic or traditional numerical techniques. In this study, the searching process is accelerated using golden section-based scaling factor and the constraints are handled by the penalty functions. Penalty functions are used to find the optimal solution for restrained optimization problems in the feasible region of the total search space. The resulting algorithm is named as Accelerated-SFLA. The proposal is implemented to solve the problem of optimal selection of processes. The results illustrate the efficacy of the proposal.

Differential Shuffled Frog-leaping Algorithm

Shuffled frog-leaping algorithm (SFLA) is a recent addition to the family of nature-inspired metaheuristic algorithms (NIMA). SFLA has proved its efficacy in solving intricate and real-world optimization problems. In the present study, we have hybridized SFLA into other well-known metaheuristic algorithm called differential evolution (DE) algorithm to enhance the searching capability as well as to maintain the diversity of population. Hybridization is a growing area of interest in research. The process of hybridization results into a new variant that combines the advantages of two or more metaheuristic algorithms in a judicious manner. In this paper, the new variant is named as differential SFLA (DSFLA). The proposal is implemented and shown its efficacy on the problems of optimization of chemical engineering.

Two-phase shuffled frog-leaping algorithm

Shuffled frog-leaping algorithm (SFLA) is a recent addition to the stochastic search methods that mimics the social and natural behaviour of species. The basic idea behind modelling of such algorithms is to achieve comparatively better solutions to the multifaceted optimization problems that are not easy to solve using traditional or deterministic mathematical techniques. SFLA combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA). In this study to improve the convergence speed, two modifications have been proposed firstly, initial population is generated using opposition based learning and secondly search process of SFLA is improved using scaling factor. The proposed algorithm is named as Two-Phase SFLA. The impact of the proposal is illustrated on four structural engineering design problems.

Artificial Bee Colony Application in Cost Optimization of Project Schedules in Construction

Artificial bee colony (ABC) simulates the intelligent foraging behavior of honey bees. ABC consists of three types of bees: employed, onlooker, and scout. Employed bees perform exploration and onlooker bees perform exploitation, whereas scout bees are responsible for randomly searching the food source in the feasible region. Being simple and having fewer control parameters, ABC has been widely used to solve complex multifaceted optimization problems. This study presents an application of ABC in optimizing the cost of project schedules in construction. As we know that project schedules consist of number of activities (predecessor and successor), variable cost is involved in accomplishing these activities. Therefore, scheduling these activities in terms of optimizing resources or cost-effective scheduling becomes a tedious task. The computational result demonstrates the efficacy of ABC.

Trigonometric Probability Tuning in Asynchronous Differential Evolution

Asynchronous differential evolution (ADE) has been derived from differential evolution (DE) with some variations. In ADE, the population is updated as soon as a vector with better fitness is found hence the algorithm works asynchronously. ADE leads to stronger exploration and supports parallel optimization. In this paper, ADE is incorporated with the trigonometric mutation operator to enhance the convergence rate, and the performance of the algorithm is tested for various values of trigonometric mutation probability; that is, the tuning of trigonometric mutation probability has been done to obtain its optimum setting. The proposed work is termed as ADE–trigonometric probability tuning (ADE-TPT). For tuning, the tests have been done over widely used benchmark functions referred from the literature, and the results obtained using different probabilities are compared.

Enhanced Asynchronous Differential Evolution Using Trigonometric Mutation

The Asynchronous Differential Evolution (ADE) is based on Differential Evolution (DE) with some variations. In ADE the population is updated as soon as a vector with better fitness is found hence the algorithm works asynchronously. ADE leads to stronger exploration and supports parallel optimization. In this paper ADE is embedded with the trigonometric mutation operator (TMO) to enhance the convergence rate of basic ADE. The proposed hybridized algorithm is termed as ADE-TMO. The algorithm is verified over widely used 10 benchmark functions referred from the literature. The simulated results show that ADE-TMO perform better than basic ADE and other state-of-art algorithms.

Aesthetic Differential Evolution Algorithm for Solving Computationally Expensive Optimization Problems

The applications of Differential Evolution (DE) and the attraction of researchers towards it, shows that it is a simple, powerful, efficient as well as reliable evolutionary algorithm to solve optimization problems. In this study an improved DE called aesthetic DE algorithm (ADEA) is introduced to solve Computationally Expensive Optimization (CEO) problems discussed in competition of congress of evolutionary computation (CEC) 2014. ADEA uses the concept of mirror images to produce new decorative positions. The mirror is placed near the most beautiful (global best) individual to accentuate its attractiveness (significance). Simulated statistical results demonstrate the efficiency and ability of the proposal to obtain good results.

A Comparative Study of Video Encryption Schemes

method to protect video contents from unauthorized access is known as a video encryption scheme. This paper first surveys the literature to identify most desired features of an encryption algorithm. Then a classification has been drawn according to their characteristics. After that, some of the algorithms are discussed and their working has been explained briefly. At the end a comparison has been shown displaying their performance on a few chosen parameters. KEYWORDSencryption, selective encryption, perceptual encryption