Rigorous Design of Complex Distillation Columns Using Process Simulators and the Particle Swarm Optimization Algorithm

We present a derivative-free optimization algorithm coupled with a chemical process simulator for the optimal design of individual and complex distillation processes using a rigorous tray-by-tray model. The proposed approach serves as an alternative tool to the various models based on nonlinear programming (NLP) or mixed-integer nonlinear programming (MINLP) . This is accomplished by combining the advantages of using a commercial process simulator (Aspen Hysys), including especially suited numerical methods developed for the convergence of distillation columns, with the benefits of the particle swarm optimization (PSO) metaheuristic algorithm, which does not require gradient information and has the ability to escape from local optima. Our method inherits the superstructure developed in Yeomans, H.; Grossmann, I. E.Optimal design of complex distillation columns using rigorous tray-by-tray disjunctive programming models. Ind. Eng. Chem. Res.2000, 39 (11), 4326–4335, in which the nonexisting trays are considered as simple bypasses of liquid and vapor flows. The implemented tool provides the optimal configuration of distillation column systems, which includes continuous and discrete variables, through the minimization of the total annual cost (TAC). The robustness and flexibility of the method is proven through the successful design and synthesis of three distillation systems of increasing complexity.

An evolutionary approach to the delineation of functional areas based on travel-to-work flows

This paper presents a new approach to the delineation of local labor markets based on evolutionary computation. The aim of the exercise is the division of a given territory into functional regions based on travel-to-work flows. Such regions are defined so that a high degree of inter-regional separation and of intra-regional integration in both cases in terms of commuting flows is guaranteed. Additional requirements include the absence of overlap between delineated regions and the exhaustive coverage of the whole territory. The procedure is based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. In the experimentation stage, two variations of the fitness function are used, and the process is also applied as a final stage for the optimization of the results from one of the most successful existing methods, which are used by the British authorities for the delineation of travel-to-work areas (TTWAs). The empirical exercise is conducted using real data for a sufficiently large territory that is considered to be representative given the density and variety of travel-to-work patterns that it embraces. The paper includes the quantitative comparison with alternative traditional methods, the assessment of the performance of the set of operators which has been specifically designed to handle the regionalization problem and the evaluation of the convergence process. The robustness of the solutions, something crucial in a research and policy-making context, is also discussed in the paper.

Multi-objective adaptive evolutionary strategy for tuning compilations

Tuning compilations is the process of adjusting the values of a compiler options to improve some features of the final application. In this paper, a strategy based on the use of a genetic algorithm and a multi-objective scheme is proposed to deal with this task. Unlike previous works, we try to take advantage of the knowledge of this domain to provide a problem-specific genetic operation that improves both the speed of convergence and the quality of the results. The evaluation of the strategy is carried out by means of a case of study aimed to improve the performance of the well-known web server Apache. Experimental results show that a 7.5% of overall improvement can be achieved. Furthermore, the adaptive approach has shown an ability to markedly speed-up the convergence of the original strategy.

Tuning compilations by multi-objective optimization: Application to Apache web server

Modern compilers present a great and ever increasing number of options which can modify the features and behavior of a compiled program. Many of these options are often wasted due to the required comprehensive knowledge about both the underlying architecture and the internal processes of the compiler. In this context, it is usual, not having a single design goal but a more complex set of objectives. In addition, the dependencies between different goals are difficult to be a priori inferred. This paper proposes a strategy for tuning the compilation of any given application. This is accomplished by using an automatic variation of the compilation options by means of multi-objective optimization and evolutionary computation commanded by the NSGA-II algorithm. This allows finding compilation options that simultaneously optimize different objectives. The advantages of our proposal are illustrated by means of a case study based on the well-known Apache web server. Our strategy has demonstrated an ability to find improvements up to 7.5% and up to 27% in context switches and L2 cache misses, respectively, and also discovers the most important bottlenecks involved in the application performance.