Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics

Aplicação e análise de métodos estocásticos de otimização ao modelo de múltiplas fontes pontuais ponderadas para a determinação da radiação em chamas

A determinação da emissão de radiação em chamas é um problema frequente em projetos de caldeiras, queimadores e equipamentos similares, sendo sua modelagem fundamental para controle de processos, para redução de custos e para prevenção de falhas e acidentes. Alguns modelos foram propostos, entre eles o chamado WMP, mas poucos estudos se dedicaram a desenvolvê-los e avaliá-los. Estudos anteriores buscaram relacionar os parâmetros do modelo WMP com o fenômeno da combustão e otimizá-los com a Otimização Extrema Generalizada. Existe contudo a possibilidade de que outros métodos sejam mais e cientes sem perda de qualidade. Com isso em mente, este estudo busca aplicar e avaliar o desempenho de diferentes algoritmos estocásticos de otimização ao modelo WMP. É feita uma revisão do estado da arte, sendo escolhidos cinco algoritmos para análise: Otimização de Lobos Cinzentos, Otimização de Manadas Egoístas, Algoritmo Genético, Algoritmo de Dentes-de-Leão e Otimização de Bactérias. Também é proposto um procedimento de calibração desses algoritmos baseada na metodologia de Projeto de Experimentos. O Algoritmo de Lobos Cinzentos se mostrou como o de melhor desempenho, sendo seus resultados médios e desvios padrões satisfatórios em comparação com os demais. O Algoritmo Genético e o Algoritmo de Dentes-de-Leão mostraram desempenho satisfatório, mas necessitando mais execuções para a con rmação da resposta. A Otimização de Manadas Egoístas e a Otimização de Bactérias exibiram desempenhos inferiores, com altas médias e desvios. A metodologia de Projeto de Experimentos se mostrou adequada para algoritmos com poucos parâmetros, mas perde qualidade à medida que o número de parâ- metros aumenta. Os melhores resultados foram encontrados com maiores quantidades de fontes emissoras e maiores comprimentos de distribuição, concordando com a tendência apresentada por trabalhos anteriores. O melhor resultado foi encontrado pelo Algoritmo de Dentes-de-Leão, na con guração com sete fontes emissoras e comprimento de distribui ção das fontes equivalente a 2,5 vezes o comprimento visível da chama, com um valor de função objetivo de 0,216 kW/m2.The determination of radiation emission on ames is a frequent problem designing boilers, burners and similar equipments, its modelling being therefore fundamental for process control, cost reduction and prevention of failures and accidents. Some models have been proposed, among them the WMP model, however little study has been dedicated to solve and evaluate them. Some previous researches sought to correlate WMP model parameters to the phenomenon of combustion and to optimise them with the Generalised Extreme Optimisation. However there is the possibility of other optimisation methods being more e cient without loss of quality. Having this in mind, this study aims to apply and evaluate the performance of di erent stochastic optimisation algorithms applied to the WMP model. A bibliographical review of the state of the art is made, being chosen ve methods to be analysed: Grey Wolf Optimiser, Sel sh Herds Optimiser, Genetic Algorithms, Dandelion Algorithm and Bacterial Foraging Optimisation. Also, a procedure for the calibration of these methods is proposed, based on the Design of Experiments methodology. The Grey Wolf Optimizer presents the best performance, with satisfactory mean results and standard deviations. The Genetic Algorithm and the Dandelion Algorithm showed good satisfactory results, but also the necessity of a deeper conference of results. The Sel sh Herds Optimiser and the Bacterial Foraging Optimisation had inferior performance, with higher means and deviations. The Design of Experiments methodology was satisfactory for algorithms with few parameters, but lost quality as the number of parameters increased. The best results were found for higher quantities of sources and larger distribution lengths, which agrees with tendencies presented by previous works. The best result was found by the Dandelion Algorithm, employing 7 point sources and a distribution length equivalent to 2.5 times the visible ame length, presenting an objective function value of 0.216 kW/m2

Lunar descent using sequential engine shutdown

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.Includes bibliographical references (p. 111-113).The notion of sequential engine shutdown is introduced and its application to lunar descent is motivated. The concept calls for the utilization of multiple fixed thrust engines in place of a single continuously throttleable engine. Downrange position control is provided by properly timed engine shutdowns. The principle advantage offered is the potential cost savings that would result from the elimination of the development cost of a throttleable rocket engine. Past lunar landing efforts are reviewed and provide the foundation for a baseline vehicle definition. A descent from a lunar parking orbit is assumed. The powered descent is divided into two phases, and a sequential engine shutdown-based guidance scheme is developed for the earlier phase. The guidance scheme consists of a biased ignition point and an algorithm for calculating shutdown times combined with a linear tangent steering law to provide full terminal position control. The performance of the sequential engine shutdown guidance scheme is assessed against two alternative approaches.(cont.) A statistical picture of the performance of each guidance scheme is obtained via Monte Carlo trials of a lunar descent simulation that captures, to first order, the interaction between the descent propulsion system, the navigation filter, and the guidance function, allowing a direct comparison to be made on the basis of accuracy and fuel consumption. The impact of variations in the number of engines available in the sequential engine shutdown case is analyzed. While the performance observed with sequential engine shutdown does not match that observed with a throttleable engine, the results suggest that it is a viable solution to the lunar descent guidance problem.by Philip N. Springmann.S.M

Queensland Institute of Technology: Handbook 1983

The Queensland Institute of Technology handbook gives an outline of the faculties and subject offerings available that were offered by QIT

Data Structures & Algorithm Analysis in C++

This is the textbook for CSIS 215 at Liberty University.https://digitalcommons.liberty.edu/textbooks/1005/thumbnail.jp