Desynchronization of simulation and optimization algorithms in HPC Environment

Need for scalability of an algorithm is essential, when one wants to utilize HPC infrastructure in an efficient and reasonable way. In such infrastructures, synchronization affects the efficiency of the parallel algorithms. However, one can consider introducing certain means of desynchronization in order to increase scalability. Allowing for omitting or delaying certain messages, can be easily accepted in the case of metaheuristics. Furthermore, some simulations can also follow this pattern and handle bigger environments. The paper presents a short survey of desynchronization idea, pointing out already obtained results or sketching out the future work focused on scaling the parallel and distributed computing or simulation algorithms leveraging desynchronization

Revisión sistemática: Técnicas de solución para el problema de enrutamiento vehicular en la gestión de residuos sólidos

Esta investigación aborda las técnicas de solución del problema del enrutamiento vehicular en la Gestión de Residuos Sólidos Municipales, por medio de la revisión sistemática de diversas técnicas y sus respectivos casos de aplicación, se analizó las diferentes características, particularidades y resultados de cada uno de ellos. Para la revisión sistemática se efectuó una exploración de referencias bibliográficas en diferentes bancos de datos que en su mayoría fueron halladas en Science Direct (https://www.sciencedirect.com/) y Springerlink (https://link.springer.com/). Para seleccionar los estudios que se han incluido en la presente investigación se hizo una búsqueda principalmente entre los años 2018 y 2022 predominando artículos científicos en lengua inglesa. Los estudios incluidos abarcan las técnicas de programación lineal en su mayoría, así como la técnica del algoritmo genético (SGA) y problema del vendedor ambulante (TSP), sin adentrarnos en la modelación matemática de cada uno de las técnicas estudiadas, lo que nos permitió efectuar un cotejo informativo de los resultados alcanzados en cada uno de los estudios en los que fueron aplicados, priorizando los resultados de los tiempos de recorrido, costo operativo e impactos ambientales generados. Dicha revisión sistemática concluye en que la aplicación de las técnicas de soluciones al problema de enrutamiento vehicular optimiza las circunstancias actuales en los diversos casos de estudio en relación a distancias, tiempos, costos operativos e impactos generados, considerando restricciones particulares, tipo de vehículos, distancias entre los nodos y lugares de disposición final y el tipo de residuos que se vayan a trasladar

An Efficient Ant Colony Optimization Framework for HPC Environments

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants’ solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale).This work was supported by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039/501100011033), and by Xunta de Galicia and FEDER funds of the EU (Centro de Investigación de Galicia accreditation 2019–2022, ref. ED431G 2019/01; Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). JRB acknowledges funding from the Ministry of Science and Innovation of Spain MCIN / AEI / 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS), and from MCIN / AEI / 10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R (SYNBIOCONTROL). Authors also acknowledge the Galician Supercomputing Center (CESGA) for the access to its facilities. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/3

A greedy heuristics multiple criteria model for solving multi-landfill site selection and plant propagation algorithm for improving waste collection vehicle routing solutions

This research focuses on two solid waste management (SWM) problems, particularly the landfill site selection problem (LSSP) and the waste collection vehicle routing problem (WCVRP). Solving LSSP involves the evaluation of multiple criteria to determine the best landfill location. Whereas WCVRP involves, the construction of vehicle routes for collecting waste from customers and discharging their loads at the landfills with the minimum total distance travelled. However, there are two main issues with the existing LSSP and WCVRP models. First, previous models focused only on a single landfill site based on the highest score without considering the operational costs criterion in solving LSSP. Second, the Plant Propagation Algorithm (PPA) has never been considered to solve WCVRP. Thus, this research proposes a greedy heuristics multiple criteria model which includes operational costs for solving multi-LSSP and develops PPA for improving WCVRP solutions. First, the importance levels of LSSP criteria including the operational cost criterion were determined by using a modified analytical hierarchy process. Then, a multiple criteria greedy heuristic model was proposed to construct WCVRP solutions and to find a new landfill site(s) with the minimum total operational costs. Moreover, the WCVRP solutions were improved by using PPA. Both models were tested on a WCVRP benchmark problem and a case-based scenario in Kubang Pasu, Kedah. Five candidate landfill sites were considered. The results revealed that a single landfill site (Candidate 4) was the best solution for the case-based scenario, with a 6.74% reduction in total distance travelled. As for multiple landfills, Candidates 3 and 4 were the best alternative sites. Nonetheless, different study areas generated different outputs based on the area's geographical conditions. The WCVRP solutions using PPA are comparable with other best-known solutions on the benchmark problem in terms of total distance travelled and the number of vehicles used. The proposed models were cost-effective and may facilitate the SWM authorities in identifying suitable locations for new landfill site(s) and waste vehicle routes