Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfield...
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Scramjet engines are a hypersonic airbreathing technology that offers a potential for economical and flexible space transportation in lieu of traditional rocket-based systems. Accurate prediction of inviscid flowfields is of particular importance for high-performance intake design, prior to consideration of viscous effects in the design process. Further, inviscid axisymmetric intakes serve as a base for streamline tracing, one of the most promising design methodologies for scramjet intakes. Multi-objective optimization studies have been conducted via surrogate-assisted evolutionary algorithm to gain physical insights into axisymmetric intake design in this study. The results indicate the existence of global optimum solutions that can simultaneously achieve maximum compression efficiency and minimum drag for any degree of compression in case the outflow is supersonic at the intake exit, which has been verified by theory. In addition, a correlation between compression efficiency and flow uniformity has been found and discussed quantitatively. This assures the optimality of the Busemann intakes in that they simultaneously offer high compression efficiency and uniform flow at the intake exit in the inviscid regime. (C) 2021 Elsevier Masson SAS. All rights reserved.
Multi-Objective evolutionary algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms...
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Multi-Objective evolutionary algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed.
The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high ...
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The population initialization step is a common step in the majority (or even all) of evolutionary algorithms (EAs). There are many population initialization techniques. Due to the limited population size and the high dimensionality of many problems, there is little chance to cover the promising regions in the search space. From different perspectives, this paper compares the stochastic and deterministic population initialization techniques through comparing five of the well-known population initializers: Random number generator (RNG), Latin Hypercube, Sobol, Halton, and Kronecker. Due to the presence of many constraints in real-world applications, in this paper, we are focusing only on single-objective constrained optimization problems. Specifically, the goal is to investigate if there is a significant difference between these population initialization methods. In this paper, we explain theoretically and mathematically these different population initialization techniques. Moreover, different illustrative examples and visualizations are introduced to explain the behavior of each technique and compare different techniques from different perspectives. The results show that due to the high uniformity of the low-discrepancy sequences such as the Halton and Sobol sequences, the generated points using these sequences are more evenly distributed over the space than RNG, which is the commonly used technique for initializing the populations in EAs. Practically, using a set of benchmark functions, we investigate the use of each population initialization technique for initializing different population-based evolutionary algorithms. The results of our experiments prove that with sufficient numbers of iterations, the EAs are not sensitive to the initialization methods and there are no significant differences between the mentioned population initialization methods. Further, the low discrepancy methods enhance the exploration ability of EAs in early iterations.
In recent years carbon fibre reinforced plastics (CFRP) have gained enormous popularity in aircraft applications. Since the material is very expensive, costs have to be saved through an automated production. For the m...
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In recent years carbon fibre reinforced plastics (CFRP) have gained enormous popularity in aircraft applications. Since the material is very expensive, costs have to be saved through an automated production. For the manufacturing of large structures it is often advisable to use cooperating robots. However, a major problem for the economic use of complex components is the programming of the robot paths. Manual teach-in is no feasible solution and therefore often decides if automated production is profitable. In this work, a system is presented which automatically calculates robot paths using evolutionary algorithms. The use of the proposed system allows, to reduce the commissioning time drastically and changes to the process can be made without great effort by changing the component data.
Multiobjective evolutionary algorithms (MOEAs) have witnessed prosperity in solving many-objective optimization problems (MaOPs) over the past three decades. Unfortunately, no one single MOEA equipped with given param...
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Multiobjective evolutionary algorithms (MOEAs) have witnessed prosperity in solving many-objective optimization problems (MaOPs) over the past three decades. Unfortunately, no one single MOEA equipped with given parameter settings, mating-variation operator, and environmental selection mechanism is suitable for obtaining a set of solutions with excellent convergence and diversity for various types of MaOPs. The reality is that different MOEAs show great differences in handling certain types of MaOPs. Aiming at these characteristics, this paper proposes a flexible ensemble framework, namely, ASES, which is highly scalable for embedding any number of MOEAs to promote their advantages. To alleviate the undesirable phenomenon that some promising solutions are discarded during the evolution process, a big archive that number of contained solutions be far larger than population size is integrated into this ensemble framework to record large-scale nondominated solutions, and also an efficient maintenance strategy is developed to update the archive. Furthermore, the knowledge coming from updating archive is exploited to guide the evolutionary process for different MOEAs, allocating limited computational resources for efficient algorithms. A large number of numerical experimental studies demonstrated superior performance of the proposed ASES. Among 52 test instances, the ASES performs better than all the six baseline algorithms on at least half of the test instances with respect to both metrics hypervolume and inverted generational distance.
For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control pa...
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ISBN:
(纸本)9781728145334
For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game. We propose adapting such methods for Rolling Horizon evolutionary algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent's win rate. On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach. We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.
The advent of numerical computational approaches permits evolutionary algorithms (EAs) to solve complex, real-world engineering problems. The additional modification or hybridization of such EAs in academic research a...
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The advent of numerical computational approaches permits evolutionary algorithms (EAs) to solve complex, real-world engineering problems. The additional modification or hybridization of such EAs in academic research and application demonstrates improved performance for domain-specific challenges. However, developing a new algorithm or comparison and selection of existing EAs for challenges in the field of optimization is relatively unexplored. The performance of different well-established algorithms is, therefore, investigated in this work. The selection of algorithms using nonparametric tests encompasses different categories to include- Genetic Algorithm, Particle Swarm Optimization, Harmony Search Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Firefly algorithm, Differential Evolution, and Artificial Bee Colony. These algorithms are applied to solve test functions, including unconstrained, constrained, industry specific problems, CEC 2011 real world optimization problems and selected CEC 2013 benchmark test functions. The three distinct performance metrics, namely, efficiency, reliability, and quality of solution derived using the quantitative attributes are provided to evaluate the performance of the employed EAs. The categorical assignment of performance attributes helps to compare different algorithms for a specific optimization problem while the performance metrics are useful to provide the common platform for new or hybrid EA development.
The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major proble...
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ISBN:
(纸本)9781728146850
The widespread use of computing devices and the heavy dependence on the internet has evolved the cyberspace to a cyber world - something comparable to an artificial world. This paper focuses on one of the major problems of the cyber world - cyber security or more specifically computer malware. We show that computer malware is a perfect example of an artificial ecosystem with a co-evolutionary predator-prey framework. We attempt to merge the two domains of biologically inspired computing and computer malware. Under the aegis of proactive defense, this paper discusses the possibilities, challenges and opportunities in fusing evolutionary computing techniques with malware creation.
We address the task of repairing infeasibility in the context of infeasible job shop scheduling problems with a hard constraint on the maximum makespan allowed. For this purpose, we adopt a job-based view of repairs, ...
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We address the task of repairing infeasibility in the context of infeasible job shop scheduling problems with a hard constraint on the maximum makespan allowed. For this purpose, we adopt a job-based view of repairs, that allows for dropping some of the jobs and so gives rise to the problem of computing the largest subset of jobs that can be scheduled under the makespan constraint. Recent work proposed a genetic algorithm for solving this problem, which integrates an efficient solution builder for defining the search space. In this paper, we build on this earlier work and make several contributions. We provide a formal analysis of both the search space and the solution builder. Then, we propose two important enhancements to the genetic algorithm: first, we develop a new solution builder aimed at reducing the number of feasibility tests, making the search process more efficient. In addition, we propose a more effective procedure for testing the feasibility of different subsets of jobs under the given makespan constraint based on the use of a light-weight genetic algorithm. Experimental results show that the proposed methods are effective at solving the problem, and that the enhancements bring significant improvements.
Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a g...
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Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a group of cells that are directly or indirectly associated with the function;thus, the user-defined values will be optimized. In the present work, 13 existing species in an electrolyte solution have been considered to predict the activity coefficient of inorganic ions in the electrolyte solution, which includes H2O, CO2(aq), H+, Na+, Ba2+, Ca2+, Sr2+, Mg2+, OH-, Cl-, SO4, CO3, HCO3. In this study, to predict the activity coefficient of species in the system, Extended UNIQUAC activity coefficient model was considered and its parameters optimized using Excel solver tool based on GRG algorithm. Total error for optimization of adjustable parameters of Extended UNIQUAC model for 13 desired ions at three temperatures 298.15 K, 323.15 K and 373.15 K with the Excel solver tool was 0.0087. The results of GRG algorithm were favorable than those of ICA, PSO and ABC algorithms. The results of this optimization are intended to predict mineral deposition. The number of adjustable variables (model parameters for optimization) is over 200, and the number of target functions is 39.
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