Affinity propagation (AP) is a clustering algorithm which has much better performance than traditional clustering approach such as K-means algorithm. AP can usually find a moderate clustering number, but “moderate” usually may not be the “optimal”. If we have found the optimal clustering number of AP, to estimate the input “preferences” (p) and the effective corresponding “preferences” (p) inte...

This paper proposes a refined version of particle swarm optimization technique for the optimum design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by t...

Feature selection is the process of removing the irrelevant features from the datasets and fuzzy clustering of microarray data are the most fascinating machine learning techniques in the real world. The main objective of this paper is selecting the independent components of the microarray data using Independent Component Analysis in order to improve the effectiveness and accuracy of the Fuzzy P...

This paper introduces a new hybrid algorithmic nature inspired approach based on the concepts of the Honey Bees Mating Optimization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO algorithm ...

One of the main challenges of wireless sensor network is how to improve its life time. The limited energy of nodes is the main obstacle. We may overcome this problem by optimizing the nodes' power consumption. A solution is clustering, but optimum clustering of wireless sensor network is an NP-Hard problem. This paper proposes a hybrid algorithm based on Genetic Algorithm and Particle Swarm Opt...

This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game the...

Dynamic structural responses via time history analysis are highly dependent to characteristics of selected records as the seismic excitation. Ground motion scaling is a well-known solution to reduce such a dependency and increase reliability to the dynamic results. The present work, formulate a twofold problem for optimal spectral matching and performing consequent sizing optimization based on ...

Spatio-temporal trajectory clustering can extract behavior and moving pattern of object with the change of time and space by exploring similar trajectories. Most of trajectory clustering method can be achieved by expanding the traditional clustering algorithms. Considering the limitations of fitness and optimization of most clustering algorithms, especially for spatio-temporal trajectory data s...

the paper studies optimization of shell-and-tube heat exchangers using the particle swarm optimization technique. a total cost function is formulated based on initial and annual operating costs of the heat exchangers. six variables – shell inside diameter, tube diameter, baffle spacing, baffle cut, number of tube passes and tube layouts (triangular or square) – are considered as the design para...

Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...