Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism

Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.

Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm

When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.

PCR Targeting of Antibiotic Resistant Bacteria in Public Drinking Water of Lahore Metropolitan,Pakistan

Objective To investigate the prevalence of kanamycin （kan） and ampicillin （amp） resistant bacteria in public drinking water. Methods Bacteria containing kan and amp resistant genes were amplified by PCR and further characterized by colony hybridization and transformation studies. The genus of kan and amp resistant bacteria was determined with standard methods. Results Among the 625 drinking water samples, 400 contained kan and amp resistant bacteria and the percentage was 42.5% and 57.5%, respectively, which was further confirmed by the amplification of a 810 bp kan resistant gene and a 850 bp amp resistant gene. Of the 170 kan resistant bacteria, 90 were Gram negative and 80 were Gram positive. Of the 230 amp resistant bacteria, 160 were Gram negative while 70 were Gram positive. Salmonella, Shigella, Staphylococcus, Streptococcus, and E.coli were detected as 13%, 11%, 17%, 30%, and 29%, respectively. Bacterial strain DH5α transformed with plasmids isolated from kan and amp resistant bacteria confirmed that the antibiotic resistant genes were mediated by plasmids. Conclusion Drinking water is contaminated with kan and amp resistant bacteria due to poor sanitary conditions.

Research on a Task Planning Method for Multi-Ship Cooperative Driving

A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving(MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm(GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm(GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm(ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.