In light of the accuracy of particle swarm optimization-particle filter (PSO-PF) inadequate for multi-robot cooperative positioning, the paper presents population density particle swarm optimization-particle filter (PDPSO-PF), which draws cooperative coevolutionary algorithm in ecology into particle swarm optimization. By taking full account of the competitive relationship between the environme...

PSO is a robust and stochastic optimization technique based on the movement and the intelligence of the swarms. PSO is inspired with social behavior of bird flocking and fish schooling. PSO algorithm is applied to solve the real world complex optimization problems. So many real world optimization problems are solved by using different combinations and variations of PSO algorithms. In this paper...

-Particle swarm optimization (PSO) is undoubtedly one of the most widely used swarm intelligence algorithm. Generally, each particle is assigned an initial value randomly. In this paper an improved PSO clustering algorithm based on affinity propagation (APPSO) is proposed which provides new ideas and methods for cluster analysis. Firstly the proposed algorithm get initial cluster centers by aff...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

Particle swarm optimization (PSO) is becoming one of the most important swarm intelligent paradigms for solving global optimization problems. Although some progress has been made to improve PSO algorithms over the last two decades, additional work is still needed to balance parameters to achieve better numerical properties of accuracy, efficiency, and stability. In the optimal PSO algorithm, th...

This study presents a robust and rigorous method based on intelligent models, namely radial basis function networks optimized by particle swarm optimization (PSO-RBF), multilayer perceptron neural networks (MLP-NNs), and adaptive neuro-fuzzy inference system optimized by particle swarm optimization methods (PSO-ANFIS), for predicting the equilibrium and kinetics of the adsorption of sulfur and ...

A branch and bound-PSO hybrid algorithm for solving integer separable concave programming problems is proposed, in which the lower bound of the optimal value was determined by solving linear programming relax and the upper bound of the optimal value and the best feasible solution at present were found and renewed with particle swarm optimization (PSO). It is shown by the numerical results that ...

Data clustering is a powerful technique for discerning the structure of and simplifying the complexity of large scale data. An improved technique combining chaotic map particle swarm optimization (CPSO) with an acceleration strategy, is proposed in this paper. Accelerated chaotic particle swarm optimization (ACPSO) searches for cluster centers of an arbitrary data set and can effectively find t...

A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO...

Swarm Intelligence in the form of Particle Swarm Optimization (PSO) has potential applications in electric drives. The excellent characteristics of PSO may be successfully used to optimize the performance of electric machines and electric drives in many aspects. It is estimated that, electric machines consume more than 50% of the world electric energy generated. Improving efficiency in electric...