A Genetic Algorithm Approach for the Capacitated Single Allocation P-Hub Median Problem

In this paper the Capacitated Single Allocation p-Hub Median Problem (CSApHMP) is considered. This problem has a wide range of applications within the design of telecommunication and transportation systems. A heuristic method, based on a genetic algorithm (GA) approach, is proposed for solving the CSApHMP. The described algorithm uses binary encoding and modified genetic operators. The caching technique is also implemented in the GA in order to improve its effectiveness. Computational experiments demonstrate that the GA method quickly reaches optimal solutions for hub instances with up to 50 nodes. The algorithm is also benchmarked on large scale hub instances with up to 200 nodes that are not solved to optimality so far

A Hybrid Evolutionary Algorithm for Efficient Exploration of Online Social Networks

Online social networks provide large amount of valuable data and may serve as research platforms for various social network analysis tools. In this study, we propose a mathematical model for efficient exploration of an online social network. The goal is to spend minimal amount of time searching for characteristics which define a sub-network of users sharing the same interest or having certain common property. We further develop an efficient hybrid method (HEA), based on the combination of an Evolutionary Algorithm (EA) with Local Search procedure (LS). The proposed mathematical model and hybrid method are benchmarked on real-size data set with up to 10 000 users in a considered social network. We provide optimal solutions obtained by CPLEX solver on problem instances with up to 100 users, while larger instances that were out of reach of the CPLEX were efficiently solved by the proposed hybrid method. Presented computational results show that the HEA approach quickly reaches all optimal solutions obtained by CPLEX solver and gives solutions for the largest considered instance in very short CPU time

Genetic Algorithm for Solving Uncapacitated Multiple Allocation Hub Location Problem

Hub location problems are widely used for network designing. Many variations of these problems can be found in the literature. In this paper we deal with the uncapacitated multiple allocation hub location problem (UMAHLP). We propose a genetic algorithm (GA) for solving UMAHLP that uses binary encoding and genetic operators adapted to the problem. Overall performance of GA implementation is improved by caching technique. We present the results of our computational experience on standard ORLIB instances with up to 200 nodes. The results show that GA approach quickly reaches all optimal solutions that are known so far and also gives results on some large-scale instances that were unsolved before

Ekologija komaraca roda Anopheles na području Beograda u proceni vektorskog potencijala za ponovno uspostavljanje transmisije malarije

Belgrade is situated in the area that is potentially at risk from malaria outbrakes. Until eradication, the main vector of malaria in this area was Anopheles maculipennis s. s. (previous name An. typicus) and secondary vectors were An. messeae and An. atroparvus. In this study we examined the distribution and ecology of Anopheles mosquitoes (Diptera, Culicidae) in Belgrade. Females of Anopheles mosquitoes were collected from animal shelters in Belgrade at eight locations during 2003. Egg morphology was used to identify the specimens. A total of 3704 females deposited eggs ready for identification. Three species of An. maculipennis complex were identified: An. messeae, An. atroparvus and An. maculipennis s. s.. The most abundant species were An. messeae (64%). The relative frequency of three species varied depending on the site of collection. Seasonal fluctuations of mosquitoes' species varied. Each develops in a distinct type of water, too. The three species of the An. maculipennis complex, particularly An. messeae and An. atroparvus, are considered as potential vectors of malaria in Belgrade. With the possible reintroduction of Plasmodium species due to climatic changes and increased travel to and from the countries where malaria is endemic, a more efficient vector control is necessary.Beograd je smešten u području koje je potencijalno rizično za ponovno uspostavljanje transmisije malarije. Do eradikacije malarije, glavni vektor malarije na području Beograda bio je An. maculipennis s. s. (raniji naziv An. typicus) a sekundarni vektori bili su An. messeae i An. atroparvus. Mi smo analizirali distribuciju i ekologiju komaraca roda Anopheles (Diptera, Culicidae) na području Beograda. Ženke komaraca roda Anopheles sakupljali smo tokom 2003. godine na 8 lokaliteta šireg područja Beograda. Identifikaciju vrsta vršili smo na osnovu morfologije položenih jaja. Od ukupnog broja izlovljenih ženki komaraca roda Anopheles, njih 3704 je položilo jaja, a njihovom identifikacijom nađene su tri vrste komaraca roda Anopheles, svi pripadnici Anopheles maculipennis kompleksa: An. messeae, An. atroparvus i An. maculipennis s. s.. U ukupnoj populaciji najzastupljeniji je bio An. messeae 64%, zatim An. atroparvus 21%, a najmanje Anopheles maculipennis s. s. 8%. Postojala je razlika u procentualnoj zastupljenosti ovih vrsta u ukupnoj Anopheles populaciji prema lokalitetima, po mesecima, prema izboru vodenih staništa. Prisutne vrste Anopheles komaraca, posebno An. messeae i An. atroparvus ukazuju da je Beograd receptivan za transmisiju malarije, a klimatski uslovi tokom leta pogodni su za kompletiranje sporogoničnog razvoja pripadnika roda Plasmodium

SOLVING THE UNCAPACITATED MULTIPLE ALLOCATION p-HUB CENTER PROBLEM BY GENETIC ALGORITHM

In this paper we describe a genetic algorithm (GA) for the uncapacitated multiple allocation p-hub center problem (UMApHCP). Binary coding is used and genetic operators adapted to the problem are constructed and implemented in our GA. Computational results are presented for the standard hub instances from the literature. It can be seen that proposed GA approach reaches all solutions that are proved to be optimal so far. The solutions are obtained in a reasonable amount of computational time, even for problem instances of higher dimensions.p-hub center problem, genetic algorithms, discrete location problems

Metaheuristic approaches to solving large-scale Bilevel Uncapacitated Facility Location Problem with clients' preferences

In this study, we consider a variant of the Bilevel Uncapacitated Facility Location Problem (BLUFLP), in which the clients choose suppliers based on their own preferences. We propose and compare three metaheuristic approaches for solving this problem: Particle Swarm Optimization (PSO), Simulated Annealing (SA), and a combination of Reduced and Basic Variable Neighborhood Search Method (VNS). We used the representation of solutions and objective function calculation that are adequate for all three proposed methods. Additional strategy is implemented in order to provide significant time savings when evaluating small changes of solution's code in improvement parts. Constructive elements of each of the proposed algorithms are adapted to the problem under consideration. The results of broad computational tests on modified problem instances from the literature show good performance of all three proposed methods, even on large problem dimensions. However, the obtained results indicate that the proposed VNS-based has significantly better performance compared to SA and PSO approaches, especially when solving large-scale problem instances. Computational experiments on large scale benchmarks demonstrate that the VNS-based method is fast, competitive, and able to find high-quality solutions, even for large-scale problem instances with up to 2000 clients and 2000 potential facilities within reasonable CPU times

Comparison of interpolation polynomials with divided differences, interpolation polynomials with finite differences, and quadratic functions obtained by the least squares method in modeling of chromatographic responses

A novel approach to mathematical modeling of chromatographic responses based on interpolation polynomials with divided differences and with finite differences is discussed. These interpolational techniques as well as traditionally applied second-order polynomial models obtained by least squares are compared. Interpolation techniques can be useful in situations where commonly used linear or quadratic models are not applicable: when the nature of dependence is complex or the investigated factor intervals are broad. The three analyzed modeling techniques are incorporated in a design of experiments methodology for systematic development and optimization of liquid chromatographic methods. The direct modeling of retention factors is carried out first, while the objective function for final quality measurement is calculated last. An interpolation polynomial with divided differences resulted in a high quality fit compared with the results obtained by the other two modeling approaches and succeeded in locating the desired optimum. It is shown that this modeling technique can be a useful alternative for modeling of chromatographic responses. Copyrigh