An Efficient Near Lossless Image Compression Algorithm Using Dissemination of Spatial Correlation for Remote Sensing Color Images

Wireless Personal Communications - Image transmission from space borne equipment is a challenging task owing to its restrictions on bandwidth and memory requirement. Many of the state of art...     

GPS: a constraint-based gene position procurement in chromosome for solving large-scale multiobjective multiple knapsack problems

The multiple knapsack problem (MKP) forms a base for resolving many real-life problems. This has also been considered with multiple objectives in genetic algorithms (GAs) for proving its efficiency. GAs use self-adaptability to effectively solve complex problems with constraints, but in certain cases, self-adaptability fails by converging toward an infeasible region. This pitfall can be resolved by using different existing repairing techniques; however, this cannot assure convergence toward attaining the optimal solution. To overcome this issue, gene position-based suppression (GPS) has been modeled and embedded as a new phase in a classical GA. This phase works on the genes of a newly generated individual after the recombination phase to retain the solution vector within its feasible region and to improve the solution vector to attain the optimal solution. Genes holding the highest expressibility are reserved into a subset, as the best genes identified from the current individuals by replacing the weaker genes from the subset. This subset is used by the next generated individual to improve the solution vector and to retain the best genes of the individuals. Each gene’s positional point and its genotype exposure for each region in an environment are used to fit the best unique genes. Further, suppression of expression in conflicting gene’s relies on the requirement toward the level of exposure in the environment or in eliminating the duplicate genes from the environment. TheMKP benchmark instances from the OR-library are taken for the experiment to test the new model. The outcome portrays that GPS in a classical GA is superior in most of the cases compared to the other existing repairing techniques.     

An application perspective evaluation of multi-agent system in versatile environments

Multi-agent systems (MAS) based computing is the most appropriate paradigm for the problem domain, where data, control, expertise or resources are distributed and also it is interesting to the user only if the technologies address the issues of interest to the user. The MAS has the hypothesis that the agent based computing offers better approach to manage the complex systems and process. They are large-scale systems and collaborate with one another to achieve their functions in a highly modular and flexible way. In this point of view, the work presented in this paper is an enhanced attempt to validate the MAS based on application perspective. As a test-bed, a distributed MAS for software testing is constructed such that to provide a hybrid testing environment based on variety of agents, which possibly incorporate several testing techniques. The developed framework is validated on two perspectives namely, efficiency of the application domain, i.e. software testing using MAS and efficiency of the proposed framework. The validation of the later case has been carried out on two conditions: regular working environment and exceptional working environment. The second type of validation provided the guidelines for implementing proper exception handling mechanism in the enhanced MAS, which is being developed for software testing Purpose.