The RFID network planning (RNP) problem belongs to the large-scale multi-objective hard optimization problems. RNP aims to optimize the overall read region based on a set of objectives. A novel approach of hybrid firefly algorithm was developed for multi-objective RNP problem. The technique was combining the Density Based Clustering method (DBSCAN) and firefly algorithm. Empirical tests were co...

In order to minimize the total active power loss and improve the voltage profile of the power system, several solutions have been proposed, including the integration of Distributed Generation (DG) in the radial distribution network. The location and size of DG are important, because a wrong choice has a negative impact on the system behavior. Several researchers have used many methods for solvi...

Graphs have enormous usage in software engineering, network and electrical engineering. In fact graphs drawing is a geometrically representation of information. Among graphs, trees are concentrated because of their ability in hierarchical extension as well as processing VLSI circuit. Many algorithms have been proposed for drawing binary trees within polygons. However these algorithms generate b...

The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvement...

Data clustering is useful in several areas such as machine learning, data mining, wireless sensor networks and pattern recognition. The most famous clustering approach is K-means which successfully has been utilized in numerous clustering problems, but this algorithm has some limitations such as local optimal convergence and initial point understanding. Clustering is the procedure of grouping o...

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem...