evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...

determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. it is a computationally intensive task due to the large number of simulation runs required. therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum number of...

This paper presents the performance of Integrated Bacterial Foraging Optimization and Particle Swarm Optimization (IBFO_PSO) technique in MANET routing. The BFO is a bioinspired algorithm, which simulates the foraging behavior of bacteria. It is effectively applied in improving the routing performance in MANET. In results, it is proved that the PSO integrated with BFO reduces routing delay, ene...

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

binary decision diagram (in short bdd) is an efficient data structure which has been used widely in computer science and engineering. bdd-based attack in key stream cryptanalysis is one of the best forms of attack in its category. in this paper, we propose a new key stream attack which is based on zdd(zero-suppressed bdd). we show how a zdd-based key stream attack is more efficient in time and ...

In order to solve the analysis problem more efficiently and quickly, we presented a hybrid method based on LS-SVM and Bacterial Foraging Optimization (BFO) in this study.The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper proposes a hybrid approach involving Bacteria Foraging Optimization Algorithm (BFO) and LS-SVM algorithms for pr...

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...

The Bacterial Foraging Optimization (BFO) algorithm is a biologically inspired computation technique which is based on mimicking the foraging behavior of E.coli bacteria. This paper illustrates how a BFO algorithm can be constructed and applied to solve parameter estimation of a EGARCH-M model which is then used for calibration of a volatility option pricing model. The results from the algorith...

The design and implementation of Power System Stabilizer (PSS) in a multimachine power system based on innovative evolutionary algorithm plainly as Breeder Genetic Algorithm (BGA) with Adaptive Mutation is described in this paper. For the analysis purpose a Conventional Power System Stabilizer and a Conventional Genetic Algorithm based Power System Stabilizer are designed and implemented in the...