New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry b...
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New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry by using an ad hoc search space and genetic operators. The new OsC2 and Os2C structures have a lower enthalpy than any known so far. Moreover, the layered pattern of OsC2 serves as a blueprint for building new crystals by adding or removing layers of carbon and/or osmium and generating many other Os + C structures like Os2C, OsC, OsC2 and OsC4. These again have a lower enthalpy than all the investigated structures, including those of the present work. The mechanical, vibrational and electronic properties are discussed as well.
The optimization of pump operations has been widely studied, as it can decrease operational and maintenance costs and can reduce greenhouse gas emissions caused by the energy consumption from fossil fueled electricity...
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The optimization of pump operations has been widely studied, as it can decrease operational and maintenance costs and can reduce greenhouse gas emissions caused by the energy consumption from fossil fueled electricity sources. However, only the optimization of pump scheduling (where pumps are controlled based on times) and the optimization of simple controls (where pumps are controlled based on one condition only, e.g. the level of one tank) were previously able to be used in the EPANET2 toolkit. This paper uses a modified version of the hydraulic solver EPANET2 that enables rule-based controls (i.e. controls based on more than one condition) to be automatically changed by an optimization algorithm. This modification is particularly useful in cases where the pump operations need to be decided based on multiple conditions: typical examples are the cases where the pumps are controlled according to the water levels of multiple tanks or when both tank levels and time of day are taken into account to reduce the pumping in the peak tariff period. The new toolkit, called ETTAR (EPANET2 Toolkit to Alter Rules), is applied to a large case study, where different types of pump operations will be tested. Results show that the optimization of rule-based controls can decrease operational costs while guaranteeing robust pump controls. (C) 2016 The Authors. Published by Elsevier Ltd.
Island models in evolutionary computation solve problems by a careful interplay of independently running evolutionary algorithms on the island and an exchange of good solutions between the islands. In this work, we co...
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ISBN:
(纸本)9781450349208
Island models in evolutionary computation solve problems by a careful interplay of independently running evolutionary algorithms on the island and an exchange of good solutions between the islands. In this work, we conduct rigorous run time analyses for such island models trying to simultaneously obtain good run times and low communication effort. We improve the existing upper bounds for the communication effort (i) by improving the run time bounds via a careful analysis, (ii) by setting the balance between individual computation and communication in a more appropriate manner, and (iii) by replacing the usual communicate-with-all-neighbors approach with randomized rumor spreading, where each island contacts a randomly chosen neighbor. This epidemic communication paradigm is known to lead to very fast and robust information dissemination in many applications. Our results concern islands running simple (1+1) evolutionary algorithms, we regard d-dimensional tori and complete graphs as communication topologies, and optimize the classic test functions OneMax and LeadingOnes.
This paper presents the application of a new evolutionary algorithm technique called combined Pareto multi-objective differential evolution (CPMDE) to optimize irrigation water allocation and crop distribution under l...
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This paper presents the application of a new evolutionary algorithm technique called combined Pareto multi-objective differential evolution (CPMDE) to optimize irrigation water allocation and crop distribution under limited water availability with three different crops (maize, potatoes and groundnut) planted on a 100 ha farmland at Vaalharts irrigation scheme, South Africa. The algorithm combines methods of Pareto ranking and Pareto dominance selections to implement a novel selection scheme at each generation. The ability of CPMDE in solving unconstrained, constrained and real-world optimization problems was demonstrated. The two objectives of the model are to maximize total crop net benefit (NB) over a planting season while minimizing total irrigation water allocation. A set of non-dominated solutions with the high NBs at lower irrigation water allocation for three crop types was obtained, and compromise programming approach was used in evaluating the most favourable solution. The best solution shows that maize produced the highest crop yield under limited water allocation in the study area. Comparing this result with that of a previous study which adopted a multi-objective optimization algorithm called multi-objective differential evolution algorithm, CPMDE is a good and robust alternative algorithm suitable for resolving crop distribution under limited water availability.
This paper presents a study on optimized control for a magnetically levitated (MAGLEV) suspension system. Unstable magnetically levitated system is modelled and integer order PID (IOPID) and fractional order PID (FOPI...
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Two new multi-objective differential evolution (DE) algorithms are used to optimize heterogeneous low-enriched uranium + mixed oxide fuel assemblies for use in a pressurized water reactor. The objectives were to maxim...
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ISBN:
(纸本)9780791857878
Two new multi-objective differential evolution (DE) algorithms are used to optimize heterogeneous low-enriched uranium + mixed oxide fuel assemblies for use in a pressurized water reactor. The objectives were to maximize plutonium content and minimize the power peaking factor. A performance comparison to a genetic algorithm is used to evaluate the applicability of DE algorithms to nuclear fuel assembly design optimization problems. Results show that DE performs highly competitively against a more established algorithm and can arguably better represent the trade-off between both objectives through greater variety in the number of different pin arrangements explored and a higher reliability in finding the 'true' Pareto-front.
Multi-objective optimization is currently an active area of research, due to the difficulty of obtaining diverse and high-quality solutions quickly. Focusing on the diversity or quality aspect means deterioration of t...
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ISBN:
(纸本)9789897582189
Multi-objective optimization is currently an active area of research, due to the difficulty of obtaining diverse and high-quality solutions quickly. Focusing on the diversity or quality aspect means deterioration of the other, while optimizing both results in impractically long computational times. This gives rise to approximate measures, which relax the constraints and manage to obtain good-enough results in suitable running times. One such measure, epsilon-dominance, relaxes the criteria by which a solution dominates another. Combining this measure with genetic programming, an evolutionary algorithm that is flexible and can solve sophisticated problems, makes it potentially useful in solving difficult optimization problems. Preliminary results on small problems prove the efficacy of the method and suggest its potential on problems with more objectives.
The current development in science and civilization consists of searching for solutions to enhance our life, security, economy, finance and health while protecting environment. In recent years, we have witnessed the a...
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ISBN:
(纸本)9781509039609
The current development in science and civilization consists of searching for solutions to enhance our life, security, economy, finance and health while protecting environment. In recent years, we have witnessed the arrival of complex machines with structures similar to humans known as humanoid robots. The combination of these technologies and optimization technics may result in robust, safe, reliable, and flexible machines that can substitute for humans in multiple difficult tasks. In order to contribute to this topic, we propose two new evolutionary algorithms based on the selfish gene theory and elitism strategies. Therefore, permanent elitism-based selfish gene algorithm (peSGA) and nonpermanent elitism based selfish gene algorithm (neSGA) are proposed. In order to validate and to evaluate the performance peSGA and neSGA, a numerical experiment is performed using IEEE CEC 2014 functions. The obtained results show that the proposed algorithms are very competitive. Furthermore, evolutionary optimization of a walking robot is formulated. The proposed algorithms are applied to the generation and control of the optimal motion of a humanoid robot.
In recent years, evolutionary algorithms based on the concept of "decomposition" have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental ...
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In recent years, evolutionary algorithms based on the concept of "decomposition" have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental in solving problems with four or more objectives, which are further classified as many-objective optimization problems. In this paper, we first review the cause-effect relationships introduced by commonly adopted schemes in such algorithms. Thereafter, we introduce a decomposition-based evolutionary algorithm with a novel assignment scheme. The scheme eliminates the need for any additional replacement scheme, while ensuring diversity among the population of candidate solutions. Furthermore, to deal with constrained optimization problems efficiently, marginally infeasible solutions are preserved to aid search in promising regions of interest. The performance of the algorithm is objectively evaluated using a number of benchmark and practical problems, and compared with a number of recent algorithms. Finally, we also formulate a practical many-objective problem related to wind-farm layout optimization and illustrate the performance of the proposed approach on it. The numerical experiments clearly highlight the ability of the proposed algorithm to deliver the competitive results across a wide range of multi-/many-objective design optimization problems.
Software systems become more and more large and complex, and therefore more dif- ficult to develop and maintain. This complexity comes from the necessity to guarantee and improve a large number of non functional prope...
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Software systems become more and more large and complex, and therefore more dif- ficult to develop and maintain. This complexity comes from the necessity to guarantee and improve a large number of non functional properties such as safety, reliability, timing performance, maintainability, cost, etc. In order to reduce software development complexity, Model Driven Engineering (MDE) proposed complementary techniques and methods. In MDE, software applications are modelled in order to express and evaluate functional and non-functional properties. How- ever, non functional properties often conflict with each other: improving a set of non func- tional properties requires to degrade other properties. Consequently, software designers must identify appropriate design decisions and apply them on valid architectural elements to produce different software architectures (or architecture alternatives). Designers must also check whether the resulting architecture alternatives fulfil a set of requirements and how they positively improve the non functional properties. Additionally, designers must compare all the alternatives regarding their impact on the NFPs, and select only those answering at best to a trade-off among the conflicting non functional properties. To create the design space of architecture alternatives, designers apply well-known solutions such as design patterns manually. This is time-consuming, error-prone and pos- sibly sub-optimal. Well-established approaches that automate the application of design patterns, were introduced by MDE. These approaches are called model transformations. A model transformation is a software artefact that specifies a set of actions to generate a target model from a source model. In this context model transformations alternatives are model transformations that generate architecture alternatives when applied to the same source model. However a problem raises when creating architectures using model transformations. Several model transformati
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