ParaDisEO-Based Design of Parallel and Distributed Evolutionary Algorithms

The original publication is available at www.springerlink.comInternational audienceParaDisEO is a framework dedicated to the design of parallel and distributed metaheuristics including local search methods and evolutionary algorithms. This paper focuses on the latter aspect. We present the three parallel and distributed models implemented in ParaDisEO and show how these can be exploited in a user-friendly, flexible and transparent way. These models can be deployed on distributed memory machines as well as on shared memory multi-processors, taking advantage of the shared memory in the latter case. In addition, we illustrate the instantiation of the models through two applications demonstrating the efficiency and robustness of the framework

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Adaptiver Suchansatz zur multidisziplinären Optimierung von Leichtbaustrukturen unter Verwendung hybrider Metaheuristik

Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. Choosing the best design and production process in the development period is the most significant link in the automobile production chain. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables. This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material. An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results. Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool. The ability of such tool to present the optimum results in multi-objective problems is improved by using some user-selected fitness functions. Finally, an exemplary sub-assembly made of sheet metal parts from a car body is optimized to enhance both, static load case and crashworthiness.Die Automobilindustrie hat in den letzten Jahren unter dem Druck von Umweltvorschriften, Sicherheitsanforderungen und wettbewerbsfähigem Markt neue Wege auf dem Gebiet der Technologien eröffnet. Leichtbau gilt als eine der innovativsten und offenkundigsten Lösungen, um Umwelt- und Sicherheitsziele zu wettbewerbsfähigen Preisen zu erreichen. Die Wahl des besten Designs und Verfahrens für Produktionen in der Entwicklungsphase ist der wichtigste Ring der Automobilproduktionskette. Um unzählige Produktionsziele zu erreichen, müssen zahlreiche Design- und Prozessparameter bewertet werden. Die Anzahl und Variation der Lösungen und Ziele sowie einige Einschränkungen wie Fertigungsbeschränkungen, finanzielle Grenzen und Fristen beeinflussen die Auswahl einer guten Kombination von Variablen. In dieser Studie werden strukturelle Optimierungswerkzeuge für aus Blech gefertigte Baugruppen, z. Karosserie, basierend auf Parametrisierung und Bewertung von Lösungen in CAD bzw. CAE. Diese Methodik konzentriert sich auf die Lösungen, die dazu führen, dass die richtige Menge an leichtem / festem Material an der richtigen Stelle der Struktur verwendet wird, anstatt vollständig ersetzt zu werden. Eine adaptive Hybrid-Metaheuristik soll verhindern, dass alle Faktoren, die Bedrohungsoptimierungstools in einem lokalen Minimum konvergieren, anstelle eines globalen Optimums. Das Auffinden des globalen Optimums wird durch einige explorative und ausbeuterische Such Heuristiken gewährleistet. Die Zuverlässigkeit, Genauigkeit und Geschwindigkeit des vorgeschlagenen Algorithmus wird mit ähnlichen Algorithmen in akademischen Optimierungsproblemen validiert und führt zu respektablen Ergebnissen. Da Strukturen möglicherweise einem weiten Bereich von Lastfällen unterliegen, z. statische, zyklische, dynamische, Temperatur usw. Möglichkeit der multidisziplinären Optimierung wurde in Optimierungswerkzeugen bereitgestellt. Um die nichtlineare Reaktion von Zielen zu überwinden und um den hohen Zeitverbrauch von FEM-Analysen in Absturzereignissen zu bewältigen, könnte ein Ersatzmodell vom Benutzer verwendet werden. Die Fähigkeit von Optimierungswerkzeugen, optimale Ergebnisse bei Problemen mit mehreren Zielsetzungen zu präsentieren, wird durch die Verwendung einiger vom Benutzer ausgewählten Fitnessfunktionen verbessert. Eine Unterbaugruppe aus Blechteilen, die zur Automobilkarosserie gehören, ist optimiert, um beide zu verbessern; statischer Lastfall und Crashsicherheit

Multiscale optimization of non-conventional composite structures for improved mechanical response

Nowadays, due to governmental requirements to control climate change, there is a great inter- est on the part of the automotive and aerospace industry to design structures as light as possible, without jeopardize their performance, thus increasing their efficiency. Multi-material design is a way to achieve this goal, as will be shown in this work In this work, multi-material design is considered with the goal of improving the structure’s stiffness, strength, and non-linear behaviour when it yields. Firstly, a microstructural topology optimization is carried out seeking for multi-material microstructures with increased stiffness and strength compared to equivalent single-material microstructures. Afterwards, this study is further extended to perform multi-scale topology optimization, where a concurrent optimization of ma- terial and structure is done. Ultimately, the non-linear behaviour of hybrid fibre reinforced com- posites is optimized in order to introduce a so-called “pseudo-ductility”. Two different optimization problems are formulated and solved here. One compliance mini- mization with mass constraint problem and another stress-based problem where the maximal von Mises stress is locally minimized in the unit-cell. The multi-material design is investigated here using two different approaches. On one hand, the two solids coexist being bonded together across sharp interfaces. On the other hand, a functionally graded material is obtained as an extensive smooth variation of material properties on account of varying composition’s volume fractions of both solids throughout the design domain. The compliance-based optimization results show that multi-material microstructures can be stiffer compared to single-material ones for the same mass requirement. Regarding the stress-based problem, lower stress peaks are obtained in bi-material design solutions and, specially, in the case of graded material solutions. As regards multi-scale topology optimization, the results show that a multi-material structure can be stiffer than its single-material counterpart, which is in accordance with the microstructural study performed earlier. Hybrid composites can achieve the so-called “pseudo-ductile” behaviour mimicking the well- known elastic-plastic behaviour. To understand under what circumstances such behaviour is ob- tained, optimization problems are formulated and solved here. Two different types of optimiza- tion problems are considered. Firstly, one finds out the optimal properties of fibres to hybridize and get the pseudo-ductile behaviour. Once an optimal hybridization is found, another optimiza- tion problem is solved in order to understand the influence of the fibre dispersion on the composite response. The optimal results obtained show hybrid composites having a considerable pseudo- ductile behaviour.Atualmente, devido às imposições governamentais para controlar as alterações climáticas, existe um grande interesse por parte da indústria automóvel e aeroespacial para o projeto de es- truturas o mais leves possíveis, sem se comprometer o seu desempenho, aumentando assim a sua eficiência. O projeto multimaterial de estruturas é um dos caminhos para se alcançar este objetivo, conforme será mostrado neste trabalho. Neste trabalho, considera-se o projeto multimaterial de estruturas com o objetivo de se melho- rar a rigidez, resistência, e comportamento não linear após cedência. Primeiro, é feita uma otimi- zação de topologia ao nível da microestrutura procurando-se microestruturas multimateriais com maior rigidez e resistência quando comparadas com microestruturas de material único equivalen- tes. Depois, este estudo é explorado também no contexto de otimização topológica multi-escala, onde é realizada uma otimização concorrente do material e estrutura. Por fim, o comportamento não linear de compósitos híbridos reforçados por fibra é otimizado com vista à introdução de um efeito de “pseudo-ductilidade”. São formulados e resolvidos aqui dois problemas diferentes de otimização. Um problema de minimização de compliance (flexibilidade) sujeito a um constrangimento de massa e outro pro- blema baseado na tensão, onde a tensão máxima de von Mises é localmente minimizada na célula unitária. O projeto multi-material é investigado aqui utilizando duas diferentes abordagens. Numa das abordagens, os dois sólidos coexistem na sua forma discreta originando-se interfaces com uma variação abrupta de propriedades. Na outra abordagem, obtém-se um material de gradiente funcional onde existe uma suave variação das propriedades obtida variando pontualmente a fração volúmica dos sólidos ao longo de todo o domínio de projeto. Os resultados da otimização baseada na compliance mostraram que microestruturas multimateriais podem ser mais rígidas quando comparadas com as de material único para o mesmo requisito de massa. Relativamente ao pro- blema baseado na tensão, são obtidos picos de tensão mais baixos nas soluções constituídas por duas fases discretas de material e, sobretudo, nas soluções de material de gradiente funcional. No que que diz respeito à otimização topológica multi-escala, os resultados mostraram que uma estrutura multimaterial pode ser mais rígida que uma estrutura de material único equivalente, o que está de acordo com o estudo realizado anteriormente ao nível da microestrutura. Os compósitos híbridos conseguem alcançar um comportamento designado de “pseudo-dúc- til”, imitando o conhecido comportamento elasto-plástico. Para melhor se compreender sob que circunstâncias tal comportamento é obtido, são formulados e resolvidos problemas de otimização. São assim considerados dois tipos diferentes de problemas de otimização. Primeiramente, desco- brem-se quais as propriedades ótimas das fibras a hibridizar, obtendo-se o comportamento pseudo-dúctil. Assim que hibridização ótima tenha sido descoberta, outro problema de otimização é resolvido de modo a perceber-se a influência da dispersão das fibras na resposta do compósito. Os resultados ótimos obtidos mostram compósitos híbridos tendo um comportamento pseudo- dúctil considerável

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications