One year ago, the coronavirus (COVID-19) virus began spreading around the world. Vaccines have now been developed by a variety companies from different countries.Vaccines must be stored in good condition within cold boxes designed to keep vaccines within acceptable temperature ranges. This paper proposes a hybrid algorithm based on a chemical reaction called Hybrid Artificial Chemical Reaction Optimization Algorithm (HACROA) which has been used to design a vaccine cold chain network in northern Thailand. The scope of this research is to focus on the Office of Disease Prevention and Control Area 1 (Chiang Mai, Thailand). The objective of this research is to rearrange routes to minimize total travel distances. Experiments were conducted to compare the efficiency of the hybrid algorithm with other algorithms in term of the distance. The optimal parameters of the algorithm employed a statistical experiment theory namely full factorial design. Experiment design and analysis were adopted to investigate the factors that affect the performance of this algorithm. HACROA was able to rearrange routes and found a better solution than the other algorithms.

In this research study, a novel metaheuristic approach using nanotechnology is proposed, known as Artificial Carbon Nanotube Synthesis Optimization (ACNSO), in order to develop a vaccine cold chain network in north of Thailand. The scope of the study emphasizes Area 1 of the Office Disease Prevention and Control in the Chiang Mai region. Vaccines must be transported both to the Provincial HealthOffices and hospitals in the region. This study seeks to arrange the transportation routes involved in order to achieve the shortest possible total distance. The algorithm must first of all assess the travel conditions between each point in the network, and then generate the starting solution. Efficient solutions to this problem will cut the total processing time. The study then made a comparison between the results produced by ACNSO algorithm and those of other algorithms used in earlier studies. Full factorial design was the statistical approach used to evaluate the optimal parameters for the algorithm. The experiment was designed to examine the various factors which influence the algorithm performance. The results showed that ACNSO algorithm found the best solution in experimental algorithms and 3 rd processing time.Index Terms-Vaccine cold chain network, metaheuristic approach, full factorial design, nanotechnology, artificial carbon nanotube synthesis optimization.

We present a novel metaheuristic-based approach for modeling an assembly of transmembrane helical bundle in membrane protein using limited distance information. Two algorithms were developed based on either a Genetic Algorithm (GA) or a Max-Min Ant System (MMAS). Both approaches were employed to solve transmembrane arrangement problems through the use of an objective penalty function based on violation parameters of distance constraints. Here, the potassium channel KcsA was used as a test case. GA and MMAS runs were carried out to find the best solution of the inner transmembrane assembly of the tetramer structure of KcsA channel using intersubunit distance constraints of backbone atoms (Sompornpisut et al. Biophys. J. 81: 2530, 2001). The resulting structures were obtained from initial transmembrane arrangements generated randomly with a set of optimization parameters that took into account translational and rotational degrees of freedom of rigid transmembrane segment. Performance, optimality and robustness of the GA and MMAS approaches were compared with existing approaches, such as Systematic Scanning (SS) and Monte Carlo algorithms (MC). It appears that the GA and MMAS methods are generally faster in finding the structure similar to the native fold than SS and MC.

No abstract

Metaheuristic methods have become a popular tool in solving large scale optimization problem for a variety of biological systems. In this report, we present Max-Min Ant System (MMAS), a class of swarm intelligence metaheuristics approach, in computing transmembrane helical arrangement of the homotetrameric protein, the potassium channel from Streptomyces iividans (KcsA). The MMAS algorithm was employed to solve transmembrane arrangement problems through the use of an objective penalty function based on distance-violated constraints. Assembly structures of the four inner helices of the KcsA channel were computed bythe construction of probability associated with a set of translational and rotational parameters and the four-fold symmetry transformation applied to the atomic coordinates of the rigid single helix. The MMAS parameters including the number of ants, the number of iteration, weight of pheromone, weight of heuristic information, and pheromone evaporation weight were examined. We demonstrated the effectiveness of the present approach, which can correctly generate native-like structure with root-mean square deviation (RMSD) below 3 Å with respect to the x-ray structure.