More and more modern metaheuristics nature–inspired algorithms are emerging and they become increasingly popular. This paper formulates an algorithm for solving the channel–allocation problem in optical wavelength division multiplexing (WDM) systems to suppress four–wave mixing crosstalk (FWM) based on a novel nature–inspired algorithm, called Cuckoo Search algorithm by using the concept of Opt...

Thresholding is a type of image segmentation, where the pixels change to make easier analyze. In bi-level thresholding, in grayscale format transformed into binary format. The traditional methods for thresholding may be inefficient finding best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied optimization different applications find optimal...

The emergence of the Semantic Web technology increases day by day due to the rapid growth of multiple web pages. Many standard formats are available to store the semantic web data. The most popular format is the Resource Description Framework (RDF). Querying large RDF graphs becomes a tedious procedure with a vast increase in the amount of data. The problem of query optimization becomes an issu...

Scientists are beginning to realize more and more that nature is a great source for inspiration in order to develop intelligent systems and algorithms. In the field of Computational Intelligence, especially Evolutionary Computation and Swarm-based systems, the degree of imitation from nature is surprisingly high and we are at the edge of developing and proposing new algorithms and/or systems, w...

Nature inspired algorithms for their powerfulness, acquire a unique place among the algorithms for optimization. This paper intends to provide a comparison of firefly algorithm (FA) with 3 other nature inspired algorithms; genetic algorithms (GA), particle swarm optimization algorithm (PSO) and ant colony systems (ACS). Traveling salesman problem (TSP) has been used as the problem to be solved ...

In many real world optimization problems, several optimization goals have to be considered in parallel. For this reason, there has been a growing interest in multi-objective optimization (MOO) in the past many years. Several new approaches have recently been proposed, which produced very good results. However, existing techniques have solved mainly problems of “low dimension”, i.e., with less t...