this study proposes a modified version of cultural algorithms (cas) which benefits from rule-based system for influence function. this rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. this is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. this rule ...

An essential aspect of efficiency control of a three-phase induction motor is the ability to generate the optimal magnetic flux required for different operating modes. In this paper, we use the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the simulated annealing (SA) to cope with the complexity of the problem and compute feasible and quasi-optimal magnetic flux ne...

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 ...

In this paper, we propose an efficient approach to design optimization of analog circuits that is based on the reinforcement learning method. In this work, Multi-Objective Learning Automata (MOLA) is used to design a two-stage CMOS operational amplifier (op-amp) in 0.25μm technology. The aim is optimizing power consumption and area so as to achieve minimum Total Optimality Index (TOI), as a new...

The genetic algorithm (GA) is an evolutionary optimization algorithm operating based upon reproduction, crossover and mutation. On the other hand, particle swarm optimization (PSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability based upon fuzzy rules. In this paper, particle swarm optimization in association with genetic a...

The genetic algorithm (GA) is an evolutionary optimization algorithm operating based upon reproduction, crossover and mutation. On the other hand, particle swarm optimization (PSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability based upon fuzzy rules. In this paper, particle swarm optimization in association with genetic a...

This paper proposes the exchange market algorithm (EMA) to solve the combined economic and emission dispatch (CEED) problems in thermal power plants. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In order to show the capabilities ...

this paper proposes a novel hybrid algorithm namely apso-bfo which combines merits of bacterial foraging optimization (bfo) algorithm and adaptive particle swarm optimization (apso) algorithm to determine the optimal pid parameters for control of nonlinear systems. to balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

Multi-objective open-shop scheduling is definitely significant in practical. However, the research focused on multi-objective open-shop scheduling was relatively scarce. This article proposed a particle swarm optimization to address open-shop scheduling problems with multiple objectives. Originally, particle swarm optimization was invented to treat continuous optimization problems. In this pape...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...