In the realm of fuzzy systems, interpretability is really appreciated in most applications, but it becomes essential in those cases in which an intensive human-machine interaction is necessary. Accuracy and interpreta...
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The various flavors of evolutionary algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmi...
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ISBN:
(纸本)9781450306904
The various flavors of evolutionary algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmic characteristics like the selection scheme can be freely exchanged between algorithms. Ultimately, the origin of the differences of the various flavors of evolutionary algorithms is rooted in the solution representation and relative genetic operators. Are these differences only superficial? Is there a deeper unity encompassing all evolutionary algorithms beyond the specific representation? Is a general mathematical framework unifying search operators for all solution representations at all possible? The aim of the tutorial is to introduce a formal, but intuitive, unified point of view on evolutionary algorithms across representations based on geometric ideas, which provides a possible answer to the above questions. It also presents the benefits for both theory and practice brought by this novel perspective. The key idea behind the geometric framework is that search operators have a dual nature. The same search operator can be defined (i) on the underlying solution representations and, equivalently, (ii) on the structure of the search space by means of simple geometric shapes, like balls and segments. These shapes are used to delimit the region of space that includes all possible offspring with respect to the location of their parents. The geometric definition of a search operator is of interest because it can be applied - unchanged - to different search spaces associated with different representations. This, in effect, allows us to define exactly the same search operator across representations in a rigorous way. The geometric view on search operators has a number of interesting consequences of which this tutorial will give a comprehensive overview. These include (i) a straightforward view on the fitness landscape seen by recombination operators, (ii) a
Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution a...
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Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimization. In this paper, we present an improved memetic differential evolution algorithm for solving global optimization problems. The proposed approach, called memetic DE (MDE), hybridizes differential evolution (DE) with a local search (LS) operator and periodic reinitialization to balance the exploration and exploitation. A new contraction criterion, which is based on the improved maximum distance in objective space, is proposed to decide when the local search starts. The proposed algorithm is compared with six well-known evolutionary algorithms on twenty-one benchmark functions, and the experimental results are analyzed with two kinds of nonparametric statistical tests. Moreover, sensitivity analyses for parameters in MDE are also made. Experimental results have demonstrated the competitive performance of the proposed method with respect to the six compared algorithms.
Efficient scheduling for the supporting operations of aircrafts in flight deck is critical to the aircraft carrier, and even several seconds' improvement may lead to totally converse outcome of a battle. In the pa...
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Efficient scheduling for the supporting operations of aircrafts in flight deck is critical to the aircraft carrier, and even several seconds' improvement may lead to totally converse outcome of a battle. In the paper, we ameliorate the supporting operations of carrierbased aircrafts and investigate three simultaneous operation relationships during the supporting process, including precedence constraints, parallel operations, and sequence flexibility. Furthermore, multifunctional aircrafts have to take off synergistically and participate in a combat cooperatively. However, their takeoff order must be restrictively prioritized during the scheduling period accorded by certain operational regulations. To efficiently prioritize the takeoff order while minimizing the total time budget on the whole takeoff duration, we propose a novel mixed integer liner programming formulation (MILP) for the flight deck scheduling problem. Motivated by the hardness of MILP, we design an improved differential evolution algorithm combined with typical local search strategies to improve computational efficiency. We numerically compare the performance of our algorithm with the classical genetic algorithm and normal differential evolution algorithm and the results show that our algorithm obtains better scheduling schemes that can meet both the operational relations and the takeoff priority requirements.
Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter a...
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Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters;some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca2+ ions with various types of solvents. This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'.
Pavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions to keep them at a reasonabl...
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Pavements heavily influence the management costs in highway networks. Operating pavements represents a challenging task involving complex decisions on the application of maintenance actions to keep them at a reasonable level of performance. The major difficulty in applying computational tools to support decision making lies in a large number of pavement sections as a result of a high length of road networks. This paper addresses maintenance scheduling for pavements by consolidating this task into two stages. In each stage, multiobjective optimization is used to optimize maintenance schedules. The main motivation is to obtain a computationally treatable model for large road networks. The first stage is defined by a collection of pavement sections composing the road network. In this stage, the performance and maintenance models are addressed. These models account for uncertainties in the future performance and effects of maintenance by defining model parameters as random variables. The second stage refers to combining maintenance schedules for individual sections to determine the optimal maintenance plan at the network level. The results obtained for the real road network demonstrate the validity and usefulness of the proposed framework. Moreover, this framework is general and can be extended to different types of infrastructure assets.
During the last decades, standards on building construction have risen sharply to integrate new, ambitious demands regarding energy efficiency, as well as thermal and optical comfort in the design procedure. Building ...
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During the last decades, standards on building construction have risen sharply to integrate new, ambitious demands regarding energy efficiency, as well as thermal and optical comfort in the design procedure. Building simulation software assists in the accurate calculation of a hypothetical or existing building's performance on several aspects;but they are, in their vast majority, assessment-oriented, rather than focused on dynamically supporting the decision-making procedure. During the last two decades, a clear shift of design professionals and academia towards addressing performance issues from the conceptual stages of a building's design is observed. In this review, the methodology of performance-driven design optimization using computational/parametric design and optimization is presented, and the core literature available on the topic is reviewed in order to identify the current status, different approaches, obstacles, and areas of future research on the subject. The review findings confirm that there is enormous potential for the design of better-performing buildings using this technique, but there are still many obstacles to overcome and areas for future research.
In this paper we present a scatter search (SS) heuristic for the optimal location, sizing and contract pricing of distributed generation (DG) in electric distribution systems. The proposed optimization approach consid...
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In this paper we present a scatter search (SS) heuristic for the optimal location, sizing and contract pricing of distributed generation (DG) in electric distribution systems. The proposed optimization approach considers the interaction of two agents: (i) the potential investor and owner of the DG, and (ii) the Distribution Company (DisCo) in charge of the operation of the network. The DG owner seeks to maximize his profits from selling energy to the DisCo, while the DisCo aims at minimizing the cost of serving the network demand, while meeting network constraints. To serve the expected demand the DisCo is able to purchase energy, through long-term bilateral contracts, from the wholesale electricity market and from the DG units within the network. The interaction of both agents leads to a bilevel programming problem that we solve through a SS heuristic. Computational experiments show that SS outperforms a genetic algorithm hybridized with local search both in terms of solution quality and computational time.
Using the evolutionary crystal structure prediction algorithm USPEX, we identify the compositions and crystal structures of thermodynamically stable compounds in the Fe-S system at pressures in the range of 100-400 GP...
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Using the evolutionary crystal structure prediction algorithm USPEX, we identify the compositions and crystal structures of thermodynamically stable compounds in the Fe-S system at pressures in the range of 100-400 GPa. We find that at pressures in the Earth's solid inner core (330-364 GPa) two compounds are stable-Fe2S and FeS. In equilibrium with iron, only Fe2S can exist in the inner core. Using the equation of state of Fe2S, we find that, in order to reproduce the density of the inner core by adding sulfur alone, 10.6-13.7 mol.% (6.4-8.4 wt.%) sulfur is needed. An analogous calculation for silicon (where the only stable compound at inner core pressures is FeSi) reproduces the density of the inner core with 9.0-11.8 mol. % (4.8-6.3 wt.%) silicon. In both cases, a virtually identical mean atomic mass (M) over bar in the range of 52.6-53.3 results for the inner core, which is much higher than (M) over bar = 49.3 inferred for the inner core from Birch's law. In the case of oxygen (allowing for the equilibrium coexistence of suboxide Fe2O with iron under core conditions), the inner core density can be explained by the oxygen content of 13.2-17.2 mol.% (4.2-5.6 wt.%), which corresponds to (M) over bar between 49.0 and 50.6. Combining our results and previous work, we arrive at four preferred compositional models of the Earth's inner core (in mol.%): (i) 86 % (Fe + Ni) + 14 % C;(ii) 84 % (Fe + Ni) + 16 % 0;(iii) 84% (Fe + Ni) + 7 % S + 9 % H;(iv) 85 % (Fe + Ni) + 6 % Si + 9 % H.
In most real-world optimization problems, it is very difficult to obtain accurate analytical objective functions derived from process mechanisms. Instead, only approximate objective functions can be built based on spa...
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ISBN:
(纸本)9781509042401
In most real-world optimization problems, it is very difficult to obtain accurate analytical objective functions derived from process mechanisms. Instead, only approximate objective functions can be built based on sparse historical data. Performance optimization of fused magnesium furnaces is a typical small data-driven optimization problem, where only very limited and noisy data is available. A surrogate-assisted data-driven evolutionary algorithm is proposed in this paper for off-line data-driven optimization of furnaces performance in magnesia production. The multiobjective evolutionary algorithm is assisted by Gaussian process models to search for Pareto optimal solutions. To generate new data samples in surrogate management, a low-order polynomial model is constructed as an approximate mechanism model that can be treated as the real fitness function. To verify the effectiveness of the proposed Gaussian process assisted evolutionary algorithm, it is first tested on nine benchmark problems in comparison with a popular multi-objective evolutionary algorithm and a surrogate-assisted evolutionary algorithm. The algorithm is then applied to a real-world fused magnesium furnaces optimization problem.
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