GA and ACO Algorithms Applied to Optimizing Location of Controllers in Wireless Networks

Optimizing location of controllers in wireless networks is an important problem in the cellular mobile networks designing. In this paper, I present two algorithms based on Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to solve it. In the first algorithm, my objective function is determined by the total distance based on finding maximum flow in a bipartite graph using Ford-Fulkerson algorithm. In the second algorithm, I generate pheromone matrix of ants and calculate the pheromone content of the path from controller i to base station j using the neighborhood includes only locations that have not been visited by ant k when it is at controller i. At each step of iterations, I choose good solutions satisfying capacity constraints and update step by step to find the best solution depending on my cost functions. I evaluate the performance of my algorithms to optimize location of controllers in wireless networks by comparing to SA, SA-Greedy, LB-Greedy algorithm. Numerical results show that my algorithms proposed have achieved much better more than other algorithms.DOI:http://dx.doi.org/10.11591/ijece.v3i2.229

Reducing Flood Risk in Changing Environments: Optimal Location and Sizing of Stormwater Tanks Considering Climate Change

[EN] In recent years, there has been an increase in the frequency of urban floods as a result of three determinant factors: the reduction in systems' capacity due to aging, a changing environment that has resulted in alterations in the hydrological cycle, and the reduction of the permeability of watersheds due to urban growth. Due to this, a question that every urban area must answer is: Are we ready to face these new challenges? The renovation of all the pipes that compose the drainage system is not a feasible solution, and, therefore, the use of new solutions is an increasing trend, leading to a new operational paradigm where water is stored in the system and released at a controlled rate. Hence, technologies, such as stormwater tanks, are being implemented in different cities. This research sought to understand how Climate Change would affect future precipitation, and based on the results, applied two different approaches to determine the optimal location and sizing of storage units, through the application of the Simulated Annealing and Pseudo-Genetic Algorithms. In this process, a strong component of computational modeling was applied in order to allow the optimization algorithms to efficiently reach near-optimal solutions. These approaches were tested in two stormwater networks at Bogota, Colombia, considering three different rainfall scenarios.This research was funded by MEXICHEM-PAVCO and COLCIENCIAS, grant number 565263339028Saldarriaga, J.; Salcedo, C.; Solarte, L.; Pulgarín, L.; Rivera, ML.; Camacho, M.; Iglesias Rey, PL.... (2020). Reducing Flood Risk in Changing Environments: Optimal Location and Sizing of Stormwater Tanks Considering Climate Change. Water. 12(9):1-24. https://doi.org/10.3390/w12092491S124129Willems, P., Arnbjerg-Nielsen, K., Olsson, J., & Nguyen, V. T. V. (2012). Climate change impact assessment on urban rainfall extremes and urban drainage: Methods and shortcomings. Atmospheric Research, 103, 106-118. doi:10.1016/j.atmosres.2011.04.003Padulano, R., Reder, A., & Rianna, G. (2019). An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy). Hydrological Processes, 33(14), 2020-2036. doi:10.1002/hyp.13449Zeroual, A., Assani, A. A., Meddi, M., & Alkama, R. (2018). Assessment of climate change in Algeria from 1951 to 2098 using the Köppen–Geiger climate classification scheme. Climate Dynamics, 52(1-2), 227-243. doi:10.1007/s00382-018-4128-0Arnbjerg-Nielsen, K., Willems, P., Olsson, J., Beecham, S., Pathirana, A., Bülow Gregersen, I., … Nguyen, V.-T.-V. (2013). Impacts of climate change on rainfall extremes and urban drainage systems: a review. Water Science and Technology, 68(1), 16-28. doi:10.2166/wst.2013.251Ashley, R. M., Balmforth, D. J., Saul, A. J., & Blanskby, J. D. (2005). Flooding in the future – predicting climate change, risks and responses in urban areas. Water Science and Technology, 52(5), 265-273. doi:10.2166/wst.2005.0142Ngamalieu-Nengoue, U. A., Martínez-Solano, F. J., Iglesias-Rey, P. L., & Mora-Meliá, D. (2019). Multi-Objective Optimization for Urban Drainage or Sewer Networks Rehabilitation through Pipes Substitution and Storage Tanks Installation. Water, 11(5), 935. doi:10.3390/w11050935Lee, E. H., & Kim, J. H. (2017). Design and Operation of Decentralized Reservoirs in Urban Drainage Systems. Water, 9(4), 246. doi:10.3390/w9040246Kändler, N., Annus, I., Vassiljev, A., & Puust, R. (2019). Peak flow reduction from small catchments using smart inlets. Urban Water Journal, 17(7), 577-586. doi:10.1080/1573062x.2019.1611888Miao, Z.-T., Han, M., & Hashemi, S. (2019). The effect of successive low-impact development rainwater systems on peak flow reduction in residential areas of Shizhuang, China. Environmental Earth Sciences, 78(2). doi:10.1007/s12665-018-8016-zMartínez, C., Sanchez, A., Galindo, R., Mulugeta, A., Vojinovic, Z., & Galvis, A. (2018). Configuring Green Infrastructure for Urban Runoff and Pollutant Reduction Using an Optimal Number of Units. Water, 10(11), 1528. doi:10.3390/w10111528Cunha, M. C., Zeferino, J. A., Simões, N. E., Santos, G. L., & Saldarriaga, J. G. (2017). A decision support model for the optimal siting and sizing of storage units in stormwater drainage systems. International Journal of Sustainable Development and Planning, 12(01), 122-132. doi:10.2495/sdp-v12-n1-122-132Ngamalieu-Nengoue, U., Iglesias-Rey, P., Martínez-Solano, F., Mora-Meliá, D., & Saldarriaga Valderrama, J. (2019). Urban Drainage Network Rehabilitation Considering Storm Tank Installation and Pipe Substitution. Water, 11(3), 515. doi:10.3390/w11030515Cimorelli, L., Morlando, F., Cozzolino, L., Covelli, C., Della Morte, R., & Pianese, D. (2016). Optimal Positioning and Sizing of Detention Tanks within Urban Drainage Networks. Journal of Irrigation and Drainage Engineering, 142(1), 04015028. doi:10.1061/(asce)ir.1943-4774.0000927Duan, H.-F., Li, F., & Yan, H. (2016). Multi-Objective Optimal Design of Detention Tanks in the Urban Stormwater Drainage System: LID Implementation and Analysis. Water Resources Management, 30(13), 4635-4648. doi:10.1007/s11269-016-1444-1Iglesias-Rey, P. L., Martínez-Solano, F. J., Saldarriaga, J. G., & Navarro-Planas, V. R. (2017). Pseudo-genetic Model Optimization for Rehabilitation of Urban Storm-water Drainage Networks. Procedia Engineering, 186, 617-625. doi:10.1016/j.proeng.2017.03.278Martínez-Solano, F., Iglesias-Rey, P., Saldarriaga, J., & Vallejo, D. (2016). Creation of an SWMM Toolkit for Its Application in Urban Drainage Networks Optimization. Water, 8(6), 259. doi:10.3390/w8060259García, L., Barreiro-Gomez, J., Escobar, E., Téllez, D., Quijano, N., & Ocampo-Martinez, C. (2015). Modeling and real-time control of urban drainage systems: A review. Advances in Water Resources, 85, 120-132. doi:10.1016/j.advwatres.2015.08.007Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., … Roeckner, E. (2013). Atmospheric component of the MPI‐M Earth System Model: ECHAM6. Journal of Advances in Modeling Earth Systems, 5(2), 146-172. doi:10.1002/jame.20015Magi, B. I. (2015). Global Lightning Parameterization from CMIP5 Climate Model Output. Journal of Atmospheric and Oceanic Technology, 32(3), 434-452. doi:10.1175/jtech-d-13-00261.1Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevliakova, E., … Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics. Journal of Climate, 25(19), 6646-6665. doi:10.1175/jcli-d-11-00560.1Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., … Chauvin, F. (2012). The CNRM-CM5.1 global climate model: description and basic evaluation. Climate Dynamics, 40(9-10), 2091-2121. doi:10.1007/s00382-011-1259-yAckerley, D., & Dommenget, D. (2016). Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures. Geoscientific Model Development, 9(6), 2077-2098. doi:10.5194/gmd-9-2077-2016Yazdi, J., Lee, E. H., & Kim, J. H. (2015). Stochastic Multiobjective Optimization Model for Urban Drainage Network Rehabilitation. Journal of Water Resources Planning and Management, 141(8), 04014091. doi:10.1061/(asce)wr.1943-5452.0000491Javier Martínez-Solano, F., Iglesias-Rey, P. L., Mora Meliá, D., & Ribelles-Aguilar, J. V. (2018). Combining Skeletonization, Setpoint Curves, and Heuristic Algorithms to Define District Metering Areas in the Battle of Water Networks District Metering Areas. Journal of Water Resources Planning and Management, 144(6), 04018023. doi:10.1061/(asce)wr.1943-5452.0000938Baek, H., Ryu, J., Oh, J., & Kim, T.-H. (2015). Optimal design of multi-storage network for combined sewer overflow management using a diversity-guided, cyclic-networking particle swarm optimizer – A case study in the Gunja subcatchment area, Korea. Expert Systems with Applications, 42(20), 6966-6975. doi:10.1016/j.eswa.2015.04.049McEnery, J. A., & Morris, C. D. (2011). Muskingum optimisation used for evaluation of regionalised stormwater detention. Journal of Flood Risk Management, 5(1), 49-61. doi:10.1111/j.1753-318x.2011.01125.xCunha, M. C., Zeferino, J. A., Simões, N. E., & Saldarriaga, J. G. (2016). Optimal location and sizing of storage units in a drainage system. 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Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A Genetic Algorithm for the Vehicle Routing Problem

The purpose of this research was to develop a version of a genetic algorithm (GA ) which would provide near optimal solutions for Vehicle Routing Problems (VRP) with both time and weight constraints. The genetic algorithm used for the experimentation was adapted from a GA which had been developed by James Bean at the University of Michigan to solve machine scheduling problems. The VRP data sets used in this research were obtained from the literature. Various aspects of the GA were experimented with in order to develop a version which would perform consistently well for all the data sets. The results of the final version of the genetic algorithm were then compared to the results presented in the original papers. The results from this research indicated that the genetic algorithm seems to perform relatively well for smaller problems with 50 or fewer customers. However, the results seem to become progressively worse as the problem becomes larger

The design of multi-band planar inverted-F antennas for mobile handsets with the aid of a novel genetic algorithm and their specific absorption rate

Wireless communications have progressed very rapidly in recent years and mobile handsets are becoming smaller and smaller. Present-day mobile cellular communication systems include combinations of the AMPS, GSM-900, DCS-l800, PCS-1900, UMTS, and WLANs in the 2.4GHz and 5.2GHz bands. User requirements for access to the various aforementioned wireless telecommunication services have resulted in a rapid technological push to unify these different systems in a drastically decreased size single mobile handset. All this combined with strict limitations set for the energy absorbed by the users of mobile terminals has created a need for improved antenna solutions and better understanding of small antennas. The objective of this thesis is to develop novel multi-band handset antenna design solutions to satisfy the specific bandwidth requirements of mobile cellular communication systems. [Continues.

Co-pyrolysis of Chlorella vulgaris with plastic wastes: Thermal degradation, kinetics and Progressive Depth Swarm-Evolution (PDSE) neuro network-based optimization

The search of sustainable route for biofuel production from renewable biomass have garnered wide interest to seek for various routes without compromising the environment. Co-pyrolysis emerges as a promising thermochemical route that can improve the pyrolysis output from simultaneously processing more than two feedstocks in an inert atmosphere. This paper focuses on the kinetic modeling and neuro-evolution optimization in the application of catalytic co-pyrolysis of microalgae and plastic waste using HZSM-5 supported on limestone (HZSM-5/LS), in which co-pyrolysis of binary mixture of microalgae and plastic wastes (i.e. High-Density Polyethylene and Low-Density Polyethylene) was investigated over different heating rates. The results have shown a positive synergistic effect between the microalgae and polyethylene in which the apparent activation energies values have reduced significantly ( 20 kJ/mol) compared to that obtained by pyrolysis of individual microalgae component. The kinetic models reflect that the mixture of microalgae and Low-Density Polyethylene for use as co-pyrolysis feedstock requires activation energy that is 23% and 13% lower compared to that required by pure microalgae and the mixture of microalgae and High-Density Polyethylene, respectively. The Progressive Depth Swarm-Evolution (PDSE) was used for neural architecture search, which subsequently provided optimal reaction condition at 873 K can achieve 99.6 % of degradation rate using a tri-combination of LDPE (0.13 %) + HDPE (0.77 %) + MA (0.11 %) in the presence of HZSM-5/LS catalyst

Quantum annealing for vehicle routing and scheduling problems

Metaheuristic approaches to solving combinatorial optimization problems have many attractions. They sidestep the issue of combinatorial explosion; they return good results; they are often conceptually simple and straight forward to implement. There are also shortcomings. Optimal solutions are not guaranteed; choosing the metaheuristic which best fits a problem is a matter of experimentation; and conceptual differences between metaheuristics make absolute comparisons of performance difficult. There is also the difficulty of configuration of the algorithm - the process of identifying precise values for the parameters which control the optimization process. Quantum annealing is a metaheuristic which is the quantum counterpart of the well known classical Simulated Annealing algorithm for combinatorial optimization problems. This research investigates the application of quantum annealing to the Vehicle Routing Problem, a difficult problem of practical significance within industries such as logistics and workforce scheduling. The work devises spin encoding schemes for routing and scheduling problem domains, enabling an effective quantum annealing algorithm which locates new solutions to widely used benchmarks. The performance of the metaheuristic is further improved by the development of an enhanced tuning approach using fitness clouds as behaviour models. The algorithm is shown to be further enhanced by taking advantage of multiprocessor environments, using threading techniques to parallelize the optimization workload. The work also shows quantum annealing applied successfully in an industrial setting to generate solutions to complex scheduling problems, results which created extra savings over an incumbent optimization technique. Components of the intellectual property rendered in this latter effort went on to secure a patent-protected status