Advances in design and implementation of optimization software

Developing optimization software that is capable of solving large and complex real-life problems is a huge effort. It is based on a deep knowledge of four areas: theory of optimization algorithms, relevant results of computer science, principles of software engineering, and computer technology. The paper highlights the diverse requirements of optimization software and introduces the ingredients needed to fulfill them. After a review of the hardware/software environment it gives a survey of computationally successful techniques for continuous optimization. It also outlines the perspective offered by parallel computing, and stresses the importance of optimization modeling systems. The inclusion of many references is intended to both give due credit to results in the field of optimization software and help readers obtain more detailed information on issues of interest

Recent Advances in Multidisciplinary Analysis and Optimization, part 1

This three-part document contains a collection of technical papers presented at the Second NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, held September 28-30, 1988 in Hampton, Virginia. The topics covered include: helicopter design, aeroelastic tailoring, control of aeroelastic structures, dynamics and control of flexible structures, structural design, design of large engineering systems, application of artificial intelligence, shape optimization, software development and implementation, and sensitivity analysis

jHawanet: an open-source project for the implementation and assessment of multi-objective evolutionary algorithms on water distribution networks

[EN] Efficient design and management of water distribution networks is critical for conservation of water resources and minimization of both energy requirements and maintenance costs. Several computational routines have been proposed for the optimization of operational parameters that govern such networks. In particular, multi-objective evolutionary algorithms have proven to be useful both properly describing a network and optimizing its performance. Despite these computational advances, practical implementation of multi-objective optimization algorithms for water networks is an abstruse subject for researchers and engineers, particularly since efficient coupling between multi-objective algorithms and the hydraulic network model is required. Further, even if the coupling is successfully implemented, selecting the proper set of multi-objective algorithms for a given network, and addressing the quality of the obtained results (i.e., the approximate Pareto frontier) introduces additional complexities that further hinder the practical application of these algorithms. Here, we present an open-source project that couples the EPANET hydraulic network model with the jMetal framework for multi-objective optimization, allowing flexible implementation and comparison of different metaheuristic optimization algorithms through statistical quality assessment. Advantages of this project are discussed by comparing the performance of different multi-objective algorithms (i.e., NSGA-II, SPEA2, SMPSO) on case study water pump networks available in the literatureThis research and the APC were funded by the Comision Nacional de Investigacion Cientifica y Tecnologica (Conicyt), grant number 1180660Gutierrez-Bahamondes, JH.; Salgueiro, Y.; Silva-Rubio, SA.; Alsina, MA.; Mora-Melia, D.; Fuertes-Miquel, VS. (2019). jHawanet: an open-source project for the implementation and assessment of multi-objective evolutionary algorithms on water distribution networks. Water. 11(10):1-17. https://doi.org/10.3390/w111020181171110Wang, Y., Hua, Z., & Wang, L. (2018). Parameter Estimation of Water Quality Models Using an Improved Multi-Objective Particle Swarm Optimization. Water, 10(1), 32. doi:10.3390/w10010032Letting, L., Hamam, Y., & Abu-Mahfouz, A. (2017). Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization. Water, 9(8), 593. doi:10.3390/w9080593Ngamalieu-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/w11050935Morley, M. ., Atkinson, R. ., Savić, D. ., & Walters, G. . (2001). GAnet: genetic algorithm platform for pipe network optimisation. Advances in Engineering Software, 32(6), 467-475. doi:10.1016/s0965-9978(00)00107-1Van Thienen, P., & Vertommen, I. (2015). Gondwana: A Generic Optimization Tool for Drinking Water Distribution Systems Design and Operation. Procedia Engineering, 119, 1212-1220. doi:10.1016/j.proeng.2015.08.978Mala-Jetmarova, H., Sultanova, N., & Savic, D. (2017). Lost in optimisation of water distribution systems? A literature review of system operation. Environmental Modelling & Software, 93, 209-254. doi:10.1016/j.envsoft.2017.02.009Durillo, J. J., & Nebro, A. J. (2011). jMetal: A Java framework for multi-objective optimization. Advances in Engineering Software, 42(10), 760-771. doi:10.1016/j.advengsoft.2011.05.014Ravber, M., Mernik, M., & Črepinšek, M. (2017). The impact of Quality Indicators on the rating of Multi-objective Evolutionary Algorithms. Applied Soft Computing, 55, 265-275. doi:10.1016/j.asoc.2017.01.03

Integrated Software Synthesis for Signal Processing Applications

Signal processing applications usually encounter multi-dimensional real-time performance requirements and restrictions on resources, which makes software implementation complex. Although major advances have been made in embedded processor technology for this application domain -- in particular, in technology for programmable digital signal processors -- traditional compiler techniques applied to such platforms do not generate machine code of desired quality. As a result, low-level, human-driven fine tuning of software implementations is needed, and we are therefore in need of more effective strategies for software implementation for signal processing applications. In this thesis, a number of important memory and performance optimization problems are addressed for translating high-level representations of signal processing applications into embedded software implementations. This investigation centers around signal processing-oriented dataflow models of computation. This form of dataflow provides a coarse grained modeling approach that is well-suited to the signal processing domain and is increasingly supported by commercial and research-oriented tools for design and implementation of signal processing systems. Well-developed dataflow models of signal processing systems expose high-level application structure that can be used by designers and design tools to guide optimization of hardware and software implementations. This thesis advances the suite of techniques available for optimization of software implementations that are derived from the application structure exposed from dataflow representations. In addition, the specialized architecture of programmable digital signal processors is considered jointly with dataflow-based analysis to streamline the optimization process for this important family of embedded processors. The specialized features of programmable digital signal processors that are addressed in this thesis include parallel memory banks to facilitate data parallelism, and signal-processing-oriented addressing modes and address register management capabilities. The problems addressed in this thesis involve several inter-related features, and therefore an integrated approach is required to solve them effectively. This thesis proposes such an integrated approach, and develops the approach through formal problem formulations, in-depth theoretical analysis, and extensive experimentation

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Requirements for multidisciplinary design of aerospace vehicles on high performance computers

The design of aerospace vehicles is becoming increasingly complex as the various contributing disciplines and physical components become more tightly coupled. This coupling leads to computational problems that will be tractable only if significant advances in high performance computing systems are made. Some of the modeling, algorithmic and software requirements generated by the design problem are discussed