Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Open source environment to define constraints in route planning for GIS-T

Route planning for transportation systems is strongly related to shortest path algorithms, an optimization problem extensively studied in the literature. To find the shortest path in a network one usually assigns weights to each branch to represent the difficulty of taking such branch. The weights construct a linear preference function ordering the variety of alternatives from the most to the least attractive.Postprint (published version

Application of a hybrid generation/utility assessment heuristic to a class of scheduling problems

A two-stage heuristic solution approach for a class of multiobjective, n-job, 1-machine scheduling problems is described. Minimization of job-to-job interference for n jobs is sought. The first stage generates alternative schedule sequences by interchanging pairs of schedule elements. The set of alternative sequences can represent nodes of a decision tree; each node is reached via decision to interchange job elements. The second stage selects the parent node for the next generation of alternative sequences through automated paired comparison of objective performance for all current nodes. An application of the heuristic approach to communications satellite systems planning is presented

Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study.

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarmintelligence- based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.post-print888 K

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.publishedVersio

Software restructuring: understanding longitudinal architectural changes and refactoring

The complexity of software systems increases as the systems evolve. As the degradation of the system's structure accumulates, maintenance effort and defect-proneness tend to increase. In addition, developers often opt to employ sub-optimal solutions in order to achieve short-time goals, in a phenomenon that has been recently called technical debt. In this context, software restructuring serves as a way to alleviate and/or prevent structural degradation. Restructuring of software is usually performed in either higher or lower levels of granularity, where the first indicates broader changes in the system's structural architecture and the latter indicates refactorings performed to fewer and localised code elements. Although tools to assist architectural changes and refactoring are available, there is still no evidence these approaches are widely adopted by practitioners. Hence, an understanding of how developers perform architectural changes and refactoring in their daily basis and in the context of the software development processes they adopt is necessary. Current software development is iterative and incremental with short cycles of development and release. Thus, tools and processes that enable this development model, such as continuous integration and code review, are widespread among software engineering practitioners. Hence, this thesis investigates how developers perform longitudinal and incremental architectural changes and refactoring during code review through a wide range of empirical studies that consider different moments of the development lifecycle, different approaches, different automated tools and different analysis mechanisms. Finally, the observations and conclusions drawn from these empirical investigations extend the existing knowledge on how developers restructure software systems, in a way that future studies can leverage this knowledge to propose new tools and approaches that better fit developers' working routines and development processes

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints

Multi-objective design of post-tensioned concrete road bridges using artificial neural networks

[EN] In order to minimize the total expected cost, bridges have to be designed for safety and durability. This paper considers the cost, the safety, and the corrosion initiation time to design post-tensioned concrete box-girder road bridges. The deck is modeled by finite elements based on problem variables such as the cross-section geometry, the concrete grade, and the reinforcing and post-tensioning steel. An integrated multi-objective harmony search with artificial neural networks (ANNs) is proposed to reduce the high computing time required for the finite-element analysis and the increment in conflicting objectives. ANNs are trained through the results of previous bridge performance evaluations. Then, ANNs are used to evaluate the constraints and provide a direction towards the Pareto front. Finally, exact methods actualize and improve the Pareto set. The results show that the harmony search parameters should be progressively changed in a diversification-intensification strategy. This methodology provides trade-off solutions that are the cheapest ones for the safety and durability levels considered. Therefore, it is possible to choose an alternative that can be easily adjusted to each need.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (BRIDLIFE Project: BIA2014-56574-R) and the Research and Development Support Program of Universitat Politecnica de Valencia (PAID-02-15).García-Segura, T.; Yepes, V.; Frangopol, D. (2017). Multi-objective design of post-tensioned concrete road bridges using artificial neural networks. Structural and Multidisciplinary Optimization. 56(1):139-150. doi:10.1007/s00158-017-1653-0S139150561Alberdi R, Khandelwal K (2015) Comparison of robustness of metaheuristic algorithms for steel frame optimization. Eng Struct 102:40–60. doi: 10.1016/j.engstruct.2015.08.012Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur J Oper Res 181:1653–1669. doi: 10.1016/j.ejor.2006.08.008Cai H, Aref AJ (2015) A genetic algorithm-based multi-objective optimization for hybrid fiber reinforced polymeric deck and cable system of cable-stayed bridges. Struct Multidiscip Optim 52:583–594. doi: 10.1007/s00158-015-1266-4Cao MS, Pan LX, Gao YF, Novák D, Ding ZC, Lehký D, Li XL (2015) Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Comput Appl 1–8. doi: 10.1007/s00521-015-2132-4Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2016) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl. 1–12. doi: 10.1007/s00521-016-2190-2Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287. doi: 10.1016/S0045-7825(01)00323-1Coello CAC, Lamont GB, Veldhuizen DA Van (2006) Evolutionary algorithms for solving multi-objective problems. Springer-Verlag New York, IncComputers and Structures Inc. (2015) Introduction to CSiBridge. Integrated 3D bridge analysis, design and rating. Berkeley, California, USADeb K (2011) Multi-objective optimisation using evolutionary algorithms: an introduction. In: Wang L, Ng AHC, Deb K (eds) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London, pp 3–34Deb K, Nain PKS (2007) An evolutionary multi-objective adaptive meta-modeling procedure using artificial neural networks. In: Yang S, Ong Y-S, Jin Y (eds) Evolutionary computation in dynamic and uncertain environments. Springer, Berlin, pp 297–322Dong Y, Frangopol DM, Saydam D (2013) Time-variant sustainability assessment of seismically vulnerable bridges subjected to multiple hazards. Earthq Eng Struct Dyn 42:1451–1467. doi: 10.1002/eqe.2281Emmerich M, Naujoks B (2004) Metamodel assisted multiobjective optimisation strategies and their application in airfoil design. In: Parmee IC (ed) Adaptive computing in design and manufacture VI. Springer, London, pp 249–260European Committee for Standardisation (2003) EN 1991–2:2003. Eurocode 1: Actions on structures-Part 2: Traffic loads bridgesEuropean Committee for Standardisation (2005) EN1992-2:2005. Eurocode 2: Design of concrete structures- Part 2: Concrete Bridge-Design and detailing rules. BrusselsFomento M (2008) EHE-08: code on structural concrete. Ministerio de Fomento, MadridFomento M (2011) IAP-11: code on the actions for the design of road bridges. Ministerio de Fomento, MadridGarcía-Segura T, Yepes V (2016) Multiobjective optimization of post-tensioned concrete box-girder road bridges considering cost, CO2 emissions, and safety. Eng Struct 125:325–336. doi: 10.1016/j.engstruct.2016.07.012García-Segura T, Yepes V, Alcalá J (2014a) Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability. Int J Life Cycle Assess 19:3–12. doi: 10.1007/s11367-013-0614-0García-Segura T, Yepes V, Martí JV, Alcalá J (2014b) Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm. Lat Am J Solids Struct 11:1190–1205. doi: 10.1590/S1679-78252014000700007García-Segura T, Yepes V, Alcalá J, Pérez-López E (2015) Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges. Eng Struct 92:112–122. doi: 10.1016/j.engstruct.2015.03.015Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68Giannakoglou KC (2002) Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. Prog Aerosp Sci 38:43–76. doi: 10.1016/S0376-0421(01)00019-7Hare W, Nutini J, Tesfamariam S (2013) A survey of non-gradient optimization methods in structural engineering. Adv Eng Softw 59:19–28. doi: 10.1016/j.advengsoft.2013.03.001Martí JV, Yepes V, González-Vidosa F (2015) Memetic algorithm approach to designing precast-prestressed concrete road bridges with steel fiber reinforcement. J Struct Eng 141:4014114. doi: 10.1061/(ASCE)ST.1943-541X.0001058Martí JV, García-Segura T, Yepes V (2016) Structural design of precast-prestressed concrete U-beam road bridges based on embodied energy. J Clean Prod 120:231–240. doi: 10.1016/j.jclepro.2016.02.024Martinez-Martin FJ, Gonzalez-Vidosa F, Hospitaler A, Yepes V (2012) Multi-objective optimization design of bridge piers with hybrid heuristic algorithms. J Zhejiang Univ Sci A 13:420–432. doi: 10.1631/jzus.A1100304Martini K (2011) Harmony search method for multimodal size, shape, and topology optimization of structural frameworks. J Struct Eng 137:1332–1339. doi: 10.1061/(ASCE)ST.1943-541X.0000378Marti-Vargas JR, Ferri FJ, Yepes V (2013) Prediction of the transfer length of prestressing strands with neural networks. Comput Concr 12:187–209. doi: 10.12989/cac.2013.12.2.187McGee R (1999) Modeling of durability performance of Tasmanian bridges. In: Melchers R, M.G S (eds) Applications of statistics and probability: civil engineering, reliability and risk analysis. A.A. Balkema, Rotterdam, pp 297–306Papadakis VG, Roumeliotis AP, Fardis MN, Vagenas CG (1996) Mathematical modelling of chloride effect on concrete du-rability and protection measures. In: Dhir RK, Jones MR (eds) Concrete repair, rehabilitation and protection. E&FN Spon, London, pp 165–174Paya I, Yepes V, González-Vidosa F, Hospitaler A (2008) Multiobjective optimization of reinforced concrete building frames by simulated annealing. Comput Civ Infrastruct Eng 23:596–610. doi: 10.1111/j.1467-8667.2008.00561.xProtopapadakis E, Schauer M, Pierri E et al (2016) A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles. Comput Struct 162:68–79. doi: 10.1016/j.compstruc.2015.08.005Quaglia CP, Yu N, Thrall AP, Paolucci S (2014) Balancing energy efficiency and structural performance through multi-objective shape optimization: Case study of a rapidly deployable origami-inspired shelter. Energ Build 82:733–745. doi: 10.1016/j.enbuild.2014.07.063Ricart J, Hüttemann G, Lima J, Barán B (2011) Multiobjective harmony search algorithm proposals. Electron Notes Theor Comput Sci 281:51–67. doi: 10.1016/j.entcs.2011.11.025Sanad A, Saka MP (2001) Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. J Struct Eng 127:818–828. doi: 10.1061/(ASCE)0733-9445(2001)127:7(818)Sarma KC, Adeli H (1998) Cost optimization of concrete structures. J Struct Eng 124:570–578. doi: 10.1061/(ASCE)0733-9445(1998)124:5(570)Shi X (2016) Experimental and modeling studies on installation of arc sprayed Zn anodes for protection of reinforced concrete structures. Front Struct Civ Eng 10:1–11. doi: 10.1007/s11709-016-0312-7Sreehari VM, Maiti DK (2016) Buckling load enhancement of damaged composite plates under hygrothermal environment using unified particle swarm optimization. Struct Multidiscip Optim 1–11. doi: 10.1007/s00158-016-1498-yTorres-Machi C, Chamorro A, Pellicer E et al (2015) Sustainable pavement management: Integrating economic, technical, and environmental aspects in decision making. Transp Res Rec J Transp Res Board 2523:56–63. doi: 10.3141/2523-07Vu KAT, Stewart MG (2000) Structural reliability of concrete bridges including improved chloride-induced corrosion models. Struct Saf 22:313–333. doi: 10.1016/S0167-4730(00)00018-7Xu H, Gao XZ, Wang T, Xue K (2010) Harmony search optimization algorithm: application to a reconfigurable mobile robot prototype. In: Geem ZW (ed) Recent advances in harmony search algorithm. Springer, Berlin, pp 11–22Yepes V, García-Segura T, Moreno-Jiménez JM (2015a) A cognitive approach for the multi-objective optimization of RC structural problems. Arch Civ Mech Eng 15:1024–1036. doi: 10.1016/j.acme.2015.05.001Yepes V, Martí JV, García-Segura T (2015b) Cost and CO2 emission optimization of precast–prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm. Autom Constr 49:123–134. doi: 10.1016/j.autcon.2014.10.013Zavala GR, Nebro AJ, Luna F, Coello Coello CA (2013) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49:537–558. doi: 10.1007/s00158-013-0996-4Zavrtanik N, Prosen J, Tušar M, Turk G (2016) The use of artificial neural networks for modeling air void content in aggregate mixture. Autom Constr 63:155–161. doi: 10.1016/j.autcon.2015.12.009Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben AE, Bäck T, Schoenauer M, Schwefel H-P (eds) Conference on parallel problem solving from nature- PPSN V. Springer Berlin Heidelberg, Amsterdam, The Netherlands, pp 292–30