In this paper we have tried to develop an altered version of the artificial bee colony algorithm which is inspired from and combined with the meta-heuristic algorithm of firefly. In this method, we have tried to change the main equation of searching within the original ABC algorithm. On this basis, a new combined equation was used for steps of employed bees and onlooker bees. For this purpose, we had to define several new parameters for improving the quality of the proposed method. In this regard, we have introduced two new parameters to the method. The new method has been simulated within the software of MATLAB and it has also been run according to objective functions of SPHERE, GRIEWANK and ACKLEY. All these functions are standard evaluation functions that are generally used for meta-heuristic algorithms. Results that were yielded by the proposed method were better than the results of the initial algorithm and especially by increasing the number of variables of the problem, this improvement becomes even more significant.We have successfully established a better balance between concepts of exploration and exploitation, especially with increasing the repetition cycles, we have successfully controlled the concept of utilization with random parameters. Tests have been ran more than 500 times.

In this paper we have tried to develop an altered version of the artificial bee colony algorithm which is inspired from and combined with the meta-heuristic algorithm of firefly. In this method, we have tried to change the main equation of searching within the original ABC algorithm. On this basis, a new combined equation was used for steps of employed bees and onlooker bees. For this purpose, we had to define several new parameters for improving the quality of the proposed method. In this regard, we have introduced two new parameters to the method. The new method has been simulated within the software of MATLAB and it has also been run according to objective functions of SPHERE, GRIEWANK and ACKLEY. All these functions are standard evaluation functions that are generally used for meta-heuristic algorithms. Results that were yielded by the proposed method were better than the results of the initial algorithm and especially by increasing the number of variables of the problem, this improvement becomes even more significant. We have successfully established a better balance between concepts of exploration and exploitation, especially with increasing the repetition cycles, we have successfully controlled the concept of utilization with random parameters. Tests have been ran more than 500 times.

50 years after Peter Safar, the father of cardiopulmonary resuscitation (CPR), established the CPR sequence as Airway, Breathing and Circulation (ABC) in 1960, in 2010 the American Heart Association changed the CPR sequence from ABC to Circulation, Airway and Breathing (CAB) and gave priority to compression and defibrillation within seconds to minutes after cardiac arrest instead of endotracheal intubation [1]. . . .

A propionic acid fermentation process not only provides a more sustainable approach but also opens the door to propionic acid production capacity in regions with limited petroleum supplies. With fermentation, low-cost substrates can be used, such as residual biomass; reducing their concentration in nature. This process becomes interesting because from it propionic acid is considered natural. Several models have already been developed to describe the dynamics of components such as: Microorganism (biomass), nutrients (substrate), metabolites (product). However, a challenge is how to define the model that best represents the kinetic term, and therefore, there are several models for this modeling. This article's novelty is the application of the Bayesian technique (Computational Bayesian Approximation) to estimate parameters and simultaneously select the best model. Model validation was carried out considering propionic fermentation regarding experimental data from the literature, which selected the Andrews model as the best to predict the dynamic of biomass, substrate and product by the following parameters estimated = 0.192, ms = 0.005, mp = 0.017.

In this paper, the effective parameters on the ductility demand of the seismically base isolated structure are investigated, and then a relation between the strength reduction factor and the target ductility is presented. The investigation has been conducted by modelling the base isolated structure as a two degree of freedom model in the Open Sees software, and the possibility of yielding in the superstructure has been considered in the model. Results show that the period of isolator and superstructure have the most effect on the ductility demand, therefore these two parameters beside the strength reduction factor and the target ductility have been used as variables of R − m relation. A nonlinear regression model has been developed for forecasting the relation and the constant parameters of the proposed scheme has been obtained based on an optimization model solved by modified artificial bee colony (ABC) algorithm. A database including 224 models under 20 earthquake records with 2% probability of exceedance in 50 years have been generated for this purpose. Since there is not any explicit closed form formula to calculate the strength reduction factor for a specific target ductility, another optimization model has been developed to calculate the data used as input of the nonlinear regression model. The proposed relation includes two nonlinear functions and it is able to quantify the inelastic performance of base isolated structures for a wide range of earthquake records accurately.

Distributed Generation (DG) is a promising solution to many power system problems such as voltage regulation, power loss, etc. This paper presents a new methodology using Fuzzy and Artificial Bee Colony algorithm (ABC) for the placement of Distributed Generators (DG) in the radial distribution systems to reduce the real power losses and to improve the voltage profile. A two-stage methodology is used for the optimal DG placement.In the first stage, Fuzzy is used to find the optimal DG locations and in the second stage, ABC algorithm is used to find the size of the DGs corresponding to maximum loss reduction. The ABC algorithm is a new population based meta heuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The proposed method is tested on standard IEEE 33 bus test system and the results are presented and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.