Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

2D multi-objective placement algorithm for free-form components

This article presents a generic method to solve 2D multi-objective placement problem for free-form components. The proposed method is a relaxed placement technique combined with an hybrid algorithm based on a genetic algorithm and a separation algorithm. The genetic algorithm is used as a global optimizer and is in charge of efficiently exploring the search space. The separation algorithm is used to legalize solutions proposed by the global optimizer, so that placement constraints are satisfied. A test case illustrates the application of the proposed method. Extensions for solving the 3D problem are given at the end of the article.Comment: ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, San Diego : United States (2009

A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem

Optimization in an essential element in mechanical engineering and has never been an easy task. Hence, using an effective optimiser to solve these problems with high complexity is important. In this study, two metaheuristic algorithms, namely, modified flower pollination algorithm (MFPA) and carnivorous plant algorithm (CPA), were proposed. Flower pollination algorithm (FPA) is a biomimicry optimisation algorithm inspired by natural pollination. Although FPA has shown better convergence than particle swarm optimisation and genetic algorithm in the pioneering study, improving the convergence characteristic of FPA still needs more work. To speed up the convergence, modifications of: (i) employing chaos theory in the initialisation of initial population to enhance the diversity of the initial population in the search space, (ii) replacing FPA’s local search strategy with frog leaping algorithm to improve intensification, and (iii) integrating inertia weight into FPA’s global search strategy to adjust the searching ability of the global strategy, were presented. CPA, on the other hand, was developed based on the inspiration from how carnivorous plants adapt to survive in harsh environments. Both MFPA and CPA were first evaluated using twenty-five well-known benchmark functions with different characteristics and seven Congress on Evolutionary Computation (CEC) 2017 test functions. Their convergence characteristic and computational efficiency were analysed and compared with eight widely used metaheuristic algorithms, with the superiority validated using the Wilcoxon signed-rank test. The applicability of MFPA and CPA were further examined on eighteen mechanical engineering design problems and two challenging real-world applications of controlling the orientation of a five-degrees-of-freedom robotic arm and moving-object tracking in a complicated environment. For the optimisation of classical benchmark functions, CPA was ranked first. It also obtained the first rank in CEC04 and CEC07 modern test functions. Both CPA and MFPA showed promising results on the mechanical engineering design problems. CPA improved over the particle swarm optimisation algorithm in terms of the best fitness value by 69.40-95.99% in the optimisation of the robotic arm. Meanwhile, MFPA demonstrated a better tracking performance in the considered case studies by at least 52.99% better fitness function evaluation and fewer number of function evaluations as compared with the competitors

GOES-R Algorithms: A Common Science and Engineering Design and Development Approach for Delivering Next Generation Environmental Data Products

GOES-R, the next generation of the National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellite (GOES) System, represents a new technological era in operational geostationary environmental satellite systems. GOES-R will provide advanced products that describe the state of the atmosphere, land, oceans, and solar/ space environments over the western hemisphere. The Harris GOES-R Ground Segment team will provide the software, based on government-supplied algorithms, and engineering infrastructures designed to produce and distribute these next-generation data products. The Harris GOES-R Team has adopted an integrated applied science and engineering approach that combines rigorous system engineering methods, with modern software design elements to facilitate the transition of algorithms for Level 1 and 2+ products to operational software. The Harris Team GOES-R GS algorithm framework, which includes a common data model interface, provides general design principles and standardized methods for developing general algorithm services, interfacing to external data, generating intermediate and L1b and L2 products and implementing common algorithm features such as metadata generation and error handling. This work presents the suite of GOES-R products, their properties and the process by which the related requirements are maintained during the complete design/development life-cycle. It also describes the algorithm architecture/engineering approach that will be used to deploy these algorithms, and provides a preliminary implementation road map for the development of the GOES-R GS software infrastructure, and a view into the integration of the framework and data model into the final design

Bat Algorithm for Multi-objective Optimisation

Engineering optimization is typically multiobjective and multidisciplinary with complex constraints, and the solution of such complex problems requires efficient optimization algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm for solving nonlinear, global optimisation problems. In this paper, we extend this algorithm to solve multiobjective optimisation problems. The proposed multiobjective bat algorithm (MOBA) is first validated against a subset of test functions, and then applied to solve multiobjective design problems such as welded beam design. Simulation results suggest that the proposed algorithm works efficiently.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1004.417

Digital predictions of complex cylinder packed columns

A digital computational approach has been developed to simulate realistic structures of packed beds. The underlying principle of the method is digitisation of the particles and packing space, enabling the generation of realistic structures. Previous publications [Caulkin, R., Fairweather, M., Jia, X., Gopinathan, N., & Williams, R.A. (2006). An investigation of packed columns using a digital packing algorithm. Computers & Chemical Engineering, 30, 1178–1188; Caulkin, R., Ahmad, A., Fairweather, M., Jia, X., & Williams, R. A. (2007). An investigation of sphere packed shell-side columns using a digital packing algorithm. Computers & Chemical Engineering, 31, 1715–1724] have demonstrated the ability of the code in predicting the packing of spheres. For cylindrical particles, however, the original, random walk-based code proved less effective at predicting bed structure. In response to this, the algorithm has been modified to make use of collisions to guide particle movement in a way which does not sacrifice the advantage of simulation speed. Results of both the original and modified code are presented, with bulk and local voidage values compared with data derived by experimental methods. The results demonstrate that collisions and their impact on packing structure cannot be disregarded if realistic packing structures are to be obtained

Algorithm engineering for optimal alignment of protein structure distance matrices

Protein structural alignment is an important problem in computational biology. In this paper, we present first successes on provably optimal pairwise alignment of protein inter-residue distance matrices, using the popular Dali scoring function. We introduce the structural alignment problem formally, which enables us to express a variety of scoring functions used in previous work as special cases in a unified framework. Further, we propose the first mathematical model for computing optimal structural alignments based on dense inter-residue distance matrices. We therefore reformulate the problem as a special graph problem and give a tight integer linear programming model. We then present algorithm engineering techniques to handle the huge integer linear programs of real-life distance matrix alignment problems. Applying these techniques, we can compute provably optimal Dali alignments for the very first time