this paper explores the capabilities of multi-objective particle swarm optimization algorithmin a simulation-optimization model for solving waste load allocation problems. the main goals are totaltreatment costs, violation of the water quality standards and equity. in this research, the water qualitysimulation model is coupled with a multi-objective optimization model, mopso. in order to derive...

In order to resolve the multi-parameter optimization problem of finite impulse response (FIR) digital filter design, a quantum bee colony algorithm (QBCA) is proposed. The proposed QBCA is a multi-dimensional search algorithm for optimization of real numbers, and uses mechanisms of quantum evolution to update the positions of different quantum bees. Computer simulations have showed that FIR dig...

the main goal of the present study was to implement a hybrid pattern of cellular automata model and particle swarm optimization algorithm based on tm and etm+ imagery of landsat satellite from 1988 to 2010 for simulating the urban sprawling. in this study, an alternative model was implemented in two ways: the first method was based on two images (1988 and 2010) and the second one was based on t...

In this paper, we propose a PID parameter tuning of particle swarm optimization for multiobjective optimization characteristics of two regional power system PID controller design. By defining a comprehensive consideration of system output overshoot, rise time and the fitness function term steadystate error indicators, such as the ITAE, and in accordance with the performance requirements of the ...

In this paper an extensive empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The algorithm is tested on extended set of benchmarks and the results are compared to the PSO with time-varying acceleration coefficients (TVAC-PSO) and the standard genetic algorithm (GA). Key-Words: Global Optimization, Particle ...

This study proposed a novel algorithm to tune and coordinate power system stabilizers (PSSs) in multi-machine power systems. For a multi-machine power system, the coordination of the PSS parameters is generally formulated as an objective function with constraints including the damping ratio and damping factor. A novel hybrid particle swarm optimization (PSO) with the passive congregation algori...

Reader network planning (RNP) problem of radio frequency identification (RFID) system is a combinational optimization problem. In this study, we propose a genetic algorithm (GA) to solve this RNP problem. We have tested the proposed GA on several RNP problems and compare with a particle swarm optimization (PSO) method by solving the same RNPs. The comparison results demonstrate that the propose...

In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS)....

Registration or spatial normalization of diffusion tensor images plays an important role in many areas of human brain white matter research, such as analysis of Fraction Anisotropy (FA) or whiter matter tracts. More difficult than registration of scalar images, spatial normalization of tensor images requires two important parts: one is tensor interpolation, and the other is tensor reorientation...

The particle swarm optimization algorithm (PSO) has two typical problems as in other adaptive evolutionary algorithms, which are based on swarm intelligence search. To deal with the problems of the slow convergence rate and the tendency to trap into premature, an improved particle swarm optimization with decline disturbance index (DDPSO) is presented in this paper. The index was added when the ...