The present work describes a method for increasing software testing efficiency by identifying the optimal test sequences in the state machine diagram. The method employs a Meta-heuristic algorithm called Cuckoo Search to investigate best paths in the diagram. It tries to provide a technique for exhaustive coverage with minimal repetition which ensures all transitions coverage and all paths cove...

This paper proposes an extension to approach proposed in [13] for image enhancement using a combination of fuzzy logic technique and bio-inspired optimization algorithm. The transformation of the image data from RGB to HSV space has been done without altering HUE information. The image has been categorized into three regions with well tuned membership functions: underexposed, overexposed and mi...

Precedence constrained sequencing problem (PCSP) is related to locate the optimal sequence with the shortest traveling time among all feasible sequences. In PCSP, precedence relations determine sequence of traveling between any two nodes. Various methods and algorithms for effectively solving the PCSP have been suggested. In this paper we propose a cuckoo search algorithm (CSA) for effectively ...

Some parasite cuckoo species lay eggs that, to the human eye, appear to mimic the appearance of the eggs of their favourite hosts, which hinders discrimination and removal of their eggs by host species. Hitherto, perception of cuckoo-host egg mimicry has been estimated based on human vision or spectrophotometry, which does not account for what the receivers' eye (i.e. hosts) actually discrimina...

Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show...

Back propagation neural network (BPNN) algorithm is a widely used technique in training artificial neural networks. It is also a very popular optimization procedure applied to find optimal weights in a training process. However, traditional back propagation optimized with Levenberg marquardt training algorithm has some drawbacks such as getting stuck in local minima, and network stagnancy. This...