This study aimed to develop a new approach to build a functioning groundwater monitoring system by detecting a reduced set of observation wells (OWs) that optimally matches the hydraulic heads measured by other OWs wi...
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This study aimed to develop a new approach to build a functioning groundwater monitoring system by detecting a reduced set of observation wells (OWs) that optimally matches the hydraulic heads measured by other OWs within the field, namely as leader wells (LWs). The optimization models used in this work are the well-known genetic algorithm (GA) and modified genetic algorithm (MGA) and a new progressive combination (PC) model. Optimization was applied to achieve three sequential selection processes: best input combinations (BICk), LWs and core leader wells (CLWs). This approach was applied to the Assiut New Barrage (ANB), a megaproject located in Assiut city, Egypt. The results show that nine LWs among 33 OWs are adequate for regular monitoring, with a reduction ratio of 72.72%. Moreover, assigning CLWs among LWs increases the accuracy of fitting to existing OWs, and helps in understanding the spatial relationships among OWs.
There is no doubt that both determining theoretical properties and characterizing the observed behavior of an evolutionary algorithm allow us to understand when to use such an algorithm in solving a class of optimizat...
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There is no doubt that both determining theoretical properties and characterizing the observed behavior of an evolutionary algorithm allow us to understand when to use such an algorithm in solving a class of optimization problems. One of those evolutionary algorithms is the Hybrid Adaptive evolutionary Algorithm (haea). The general scheme followed by a haea algorithm is to evolve every individual of the population by selecting genetic operators according to a kind of chaotic competition mechanism. This paper proposes and studies, from both theoretical and experimental points of view, the class of hybrid adaptive evolutionary algorithms (called chavela), i.e., the class of evolutionary algorithms that follow such a general scheme. In this way, this paper presents a formal characterization of the chavela class in terms of Markov kernels;establishes convergence properties;proves that (parallel) hill-climbing algorithms belong to the chavela class;develops generational, steady-state, and classic versions;and analyzes the running behavior of chavela on well-known optimization functions.
We analyse the performance of well-known evolutionary algorithms, the (1 + 1) EA and the (1 + similar to) EA, in the prior noise model, where in each fitness evaluation the search point is altered before the evaluatio...
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We analyse the performance of well-known evolutionary algorithms, the (1 + 1) EA and the (1 + similar to) EA, in the prior noise model, where in each fitness evaluation the search point is altered before the evaluation with probability p. We present refined results for the expected optimisation time of these algorithms on the function -LeadingOnes, where bits have to be optimised in sequence. Previous work showed that the (1 + 1) EA on LeadingOnes runs in polynomial expected time if p = O((log n)/n2) and needs superpolynomial expected time if p = similar to((log n)/n), leaving a huge gap for which no results were known. We close this gap by showing that the expected optimisation time is similar to(n2) . exp(similar to(min{pn2, n})) for all p = 1/2, allowing for the first time to locate the threshold between polynomial and superpolynomial expected times at p = similar to((log n)/n2). Hence the (1 + 1) EA on -LeadingOnes is surprisingly sensitive to noise. We also show that offspring populations of size similar to = 3.42 log n can effectively deal with much higher noise than known before. Finally, we present an example of a rugged landscape where prior noise can help to escape from local optima by blurring the landscape and allowing a hill climber to see the underlying gradient. We prove that in this particular setting noise can have a highly beneficial effect on performance.
In this study, modeling of an irreversible thermoelectric heat pump was conducted, and its performance was assessed in terms of exergy for 10, 20, 30 and 40 K difference in temperature ( increment T) by changing the v...
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In this study, modeling of an irreversible thermoelectric heat pump was conducted, and its performance was assessed in terms of exergy for 10, 20, 30 and 40 K difference in temperature ( increment T) by changing the values of the design parameters. By employing this model, positive impact of increasing cross-section area, current and thermocouple's length which in turn increases the exergy efficiency is realized. In addition, diminishing adverse impact of adding more thermocouples on the exergy efficiency of the system is illustrated. Afterward, exergoeconomic performance of the thermoelectric heat pump is evaluated. Then, exergoeconomic factor for each of the system's components is diagnosed. The value of the mentioned parameter for the whole system is 60.6%, representing the ratio of the investment costs to exergy destruction costs. Considering the two objectives of reducing the unit cost of produced heat and increasing the exergy efficiency, the thermoelectric heat pump was optimized to create a temperature difference ( increment T) of 30 K by state of the art optimization algorithms such as MOPSO, SPEA2, PESA2 and response surface method (RSM). Comparing the drawn Pareto of each algorithm reveals that the Pareto drawn by the SPEA2 algorithm had better quality than the other two algorithms. Utilizing SPEA2 algorithm for this study yielded an exergoeconomic factor of 0.5 $/kWh and 14.8%, while the results obtained via evolutionary algorithms in this experiment are optimal compared to the RSM.
In order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in m...
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In order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in many disciplines, the problems in vehicle crashworthiness pose severe difficulties for standard, gradient-based approaches, due to high noisiness, multi-modality, and discontinuous nature of the nonlinear simulation responses considered typically as objectives and constraints. In this article, we propose to use evolutionary algorithms (EAs) together with a suitable low-dimensional representation in an extended version of the evolutionary Level Set Method (EA-LSM), able to address complex 3D crash TO problems. The method is used to optimize a 3D-printed metal joint in a hybrid S-rail structure under axial crash loading, inspired by novel frame design concepts in industry. The obtained results show that the method is capable of handling optimization problems with multiple constraints, including challenging acceleration responses, and yields significantly better solutions than the state-of-the-art methods. Finally, the robustness of the obtained designs is studied, demonstrating the ability of EA-LSM to find designs of low sensitivity w.r.t. small variations of the loading conditions, which is crucial from the perspective of industrial applications of the proposed method.
One of the major concerns in evolutionary algorithms is the premature convergence caused by what is known as the Exploration and Exploitation Balance problem. To maintain this balance, population diversity should be m...
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One of the major concerns in evolutionary algorithms is the premature convergence caused by what is known as the Exploration and Exploitation Balance problem. To maintain this balance, population diversity should be maintained during the initialization/optimization process. Maintaining diversity can be done through different strategies, but they commonly answer one question: when to introduce more diversity to the population? To answer this question there should be diversity metrics upon which a decision can be made to add diversity;consequently, add/reduce exploration/exploitation. There are as many diversity metrics as many problems and representations. That is, diversity metrics are very problem-specific. This work provides diversity metrics for the variable-length chromosome Genetic Algorithm for Shortest Path. The suggested metrics consider the varying lengths of the chromosomes, problem representation, and the search space. To measure chromosome-length diversity, a novel chromosome-length-based metric has been proposed. By exploiting the fact that the possible genes that can form any chromosome are well known in this specific direct-encoded population, a new simple metric that measures the representation of genes in the initial population is proposed and experimentally investigated. The presented metrics put through an extensive simulation and comparatively studied. Relationship between the proposed metrics has been quantified using Principal Component Analysis under varying network/population sizes.
Presently, blast furnace-basic oxygen furnace (BF-BOF) route is conventionally used in integrated steel plants (ISPs). As availability and cost of coking coal are becoming serious issues, alternative routes of iron an...
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Presently, blast furnace-basic oxygen furnace (BF-BOF) route is conventionally used in integrated steel plants (ISPs). As availability and cost of coking coal are becoming serious issues, alternative routes of iron and steel production such as COREX, shaft-based process for direct reduction of iron and electric arc furnace in combination with BF-BOF route is becoming a matter of focus. The possibility of energy efficiency improvement using such a mixed route in ISPs is dependent on proper utilization of fuel gases generated in addition to the total input fuel bearing resources to the plant. A flow-sheet simulation approach has been developed using phenomenological and stoichiometric modeling of the important process steps. Optimization for lowering input fuel energy and maximizing available fuel gas energy has been developed based on evolutionary algorithms using simulated flow-sheet streams. The fuel gas used downstream needs to be tailored to higher calorific values (CVs) in the gas network for critical downstream applications. Two cases of high CV-mixed fuel gas have been considered comprising (a) principally mix of coke oven gas and COREX off-gas and (b) mix of blast furnace gas and coke oven gas in 2:1 volume ratio along with high CV gases such as COREX off-gas and BOF converter gas. Energy optimization possibilities exist which simultaneously minimize input fuel energy and maximize high CV fuel gas for downstream use. The flow-sheet solutions simultaneously lead to configuration predictions with fractional stream splits.
Feature selection plays a pivotal role in handling today's high-dimensional databases by keeping only the most valuable features, leading to less computation, improved performance, and higher transparency in decis...
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Feature selection plays a pivotal role in handling today's high-dimensional databases by keeping only the most valuable features, leading to less computation, improved performance, and higher transparency in decision-making processes. Despite the considerable advances in combinatorial optimization, this data-preprocessing step is computationally NP-hard and continues to pose critical challenges, particularly for very high-dimensional (VHD) databases. Here, we propose integer coding and fuzzy granulation (FG) as an integral part of evolutionary wrapper-based feature selection. Based on this integer coding, we further propose crossover and mutation operators that employ set operations such as 'union,' 'intersection,' and 'complement' for higher transparency in their evolutionary explorative and exploitative search processes. In addition to its common use as a surrogate technique to avoid unnecessary computations by recognizing similarities, the fuzzy granulation concept also operates as a repulsive strategy that searches for dissimilarities in the elitist and population initialization routines to reach higher population diversity. An ablation study is implemented to discover the role of individual components of this multi-prong approach. The results are then compared on 22 benchmark problems, ranging from 64 to 138672 attributes, with nine competing methods. Superior performance is shown for the proposed approach in terms of accuracy (in 15 of 22 cases) and achieving a substantially smaller (as much as six times less) feature set with considerably less computational cost (by an average of 30 percent), particularly for VHD feature selection & COPY;2023 Elsevier B.V. All rights reserved.
As an effective kind of optimization technique, evolutionary algorithms (EAs) have been widely used in many fields. In the context of EAs, to determine which algorithm would be the most appropriate for the specific pr...
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Parameters optimization is a research hotspot of SVM and has gained increasing interest from various research fields. Compared with other optimization algorithms, genetic-based evolutionary algorithms that have achiev...
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Parameters optimization is a research hotspot of SVM and has gained increasing interest from various research fields. Compared with other optimization algorithms, genetic-based evolutionary algorithms that have achieved optimization according to the laws of separation and free combination in genetics are gradually attracted much attention. Also, due to the characteristics of self-organization and self-adaptation, these algorithms often enable SVM to obtain appropriate parameters, so that the model can be applied to more applications. Additionally, many improvements have been proposed in the past two decades in order to allow the optimized SVM model to obtain better performance. This work focuses on reviewing the current state of genetic-based evolutionary algorithms used to optimize parameters of SVM and its variants. First, we introduce the principles of SVM and provide a survey on optimization methods of its parameters. Then we propose a taxonomy of improving genetic-based evolutionary algorithms according to code mechanism, parameters control, population structure, evolutionary strategy, operation mechanism, operators, and many other hybrid approaches. Furthermore, this paper analyzes and compares the advantages and disadvantages of the above algorithms explicitly, and provides their applicable scenarios as well. Finally, we highlight the existing problems of genetic-based evolutionary algorithms used for parameters optimization of SVM and prospect development trends of this field in the future.
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