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Software testing is one of the essential parts of the software development life cycle. In software industry, the testing cost can be approximately 50% of the total cost of a software project so efficient ways of testing software are crucially important in reducing costs, time and effort. There are two major methods of software testing; black-box testing (focuses only what the software can do) and white-box testing (tests the internal structure of the software under consideration thoroughly and the ultimate goal is to write test cases that force the program coverage.) For program coverage, identification of suitable paths is one of the major software testing problems. These test paths are known as test sequences. Generation of automated and effective test sequences is also a very difficult task in software testing process. In the proposed work, the problem “Test Sequence Generation” is considered as a multi-objective optimization problem by having two objectives to be optimized simultaneously, Oracle Cost, and Path Priority. In real time environment, there are many constraints which have to be fulfilled when dealing with an effective testing. So, such test sequences that meet multiple objectives simultaneously are generated in order to reduce the testing efforts. To solve this problem a recently developed algorithm “Multi-Objective Firefly Algorithm (MOFA)” is used. The problem “Test Sequence Generation” is first implemented by Firefly Algorithm and later by using MOFA-considering the problem as Multi-Objective Optimization Problem. The proposed technique implementing test sequences with multiple (two) objectives and its results are presented.

Requirement Engineering (RE) is considered as an important part in Software Development Life Cycle. It is a traditional Software Engineering (SE) process. The goal of RE is to Identify, Analyze, Document and Validate requirements. Requirement Prioritization is a crucial step towards making good decisions about product plan but it is often neglected. It is observed that in many cases the product is considered as a failure without proper prioritization because it fails to meet its core objectives. When a project has tight schedule, restricted resources, and customer expectations are high then it is necessary to deploy the most critical and important features as early as possible. For this purpose requirements are prioritized. Several requirement prioritization techniques have been presented by various researchers over the past years in the domain of SE as well as Computational Intelligence. A new technique is presented in this paper which is a hybrid of both domains named as FuzzyHCV. FuzzyHCV is a hybrid of Hierarchical Cumulative Voting (HCV) and Fuzzy Expert System. Comparative analysis is performed between new technique and an existing HCV technique. Result shows that proposed technique has proved to be more reliable and accurate.

This research presents an optimization technique for multiple routes generation using simulated niche based particle swarm optimization for dynamic online route planning, optimization of the routes and proved to be an effective technique. It effectively deals with route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated niche based particle swarm optimization (SN-PSO) is proposed using modified particle swarm optimization algorithm for dealing with online route planning and is tested for randomly generated environments, obstacle ratio, grid sizes, and complex environments. The conventional techniques perform well in simple and less cluttered environments while their performance degrades with large and complex environments. The SN-PSO generates and optimizes multiple routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SN-PSO is proved to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints. The efficiency of the SN-PSO is tested in a mine field simulation with different environment configurations and successfully generates multiple feasible routes.

This paper presents a novel digital watermarking algorithm using Contourlet and Discrete Cosine transforms to perceptually shape the watermark by taking care of two conflicting parameters imperceptibility and robustness. Multi Objective Genetic Algorithm is used to decide the optimal watermarking strength (a). Contourlet transform is used to get better quality by taking care of Human Visual System and better robustness under various kinds of linear and non-linear filtering attacks. Discrete Cosine Transform with Zigzag scanning is used to detect the watermark blindly. The watermark embedding process is applied over the sub-band coefficients that lie on the edges and around the edges where distortions are less noticeable. The proposed algorithm provides an excellent tradeoff between robustness and imperceptibility and it is image adaptive. The experimental results show that the proposed algorithm improved the resistance to attacks especially JPEG compression with 5% quality, Median filter with a mask of 6 × 6, resizing up to 20% and various non-linear filtering attacks. Motivation of presented work is to adapt a watermark sequence to those frequency components of an image which provide a transparent and robust watermark.

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration

In this study we present a sub-swarm based particle swarm optimization algorithm for niching (NSPSO). The NSPSO algorithm is capable of locating and maintaining a sufficient number of niches throughout the execution of the algorithm. The niches which are identified are then exploited by using a sub-swarm strategy which tries to refine the niche and converge to an optimum solution. NSPSO is capable of locating multiple solutions and is well suited for multimodal optimization problems. From the experimentation results, we have observed that NSPSO is quite efficient in locating both global and local optima. We present a comparison of the performance of NSPSO with NichePSO and SPSO.

One of the most promising uses for multi agent systems is the searching for items or resources in unknown environments. The use of multi agent systems to locate unexploded ordinance proves to be an excellent example of one such application. This research explores the possibility of a hybrid architecture that implements mine detection, obstacle avoidance and route planning with a group of autonomous agents with coordination capabilities. Groups of inter cooperating multi agents working towards a common goal have the potential to perform a task faster and with an increased level of efficiency then the same number of agents acting in an independent manner. This coordination framework will address the issues involved during such unknown exploration