Water distribution networks (WDNs) are essential for modern cities, as effective design can reduce construction costs and ensure reliable service. As cities expand, optimizing these large networks becomes increasingly...
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Water distribution networks (WDNs) are essential for modern cities, as effective design can reduce construction costs and ensure reliable service. As cities expand, optimizing these large networks becomes increasingly complex. In this work, we introduce a novel approach by combining simulated annealing (SA) with variable neighbourhood search (VNS) into a single heuristic algorithm for WDN optimization, marking the first use of the SA-VNS method in this context. Additionally, we apply Taguchi's design of experiments (DOE) to tune the parameters of the SA-VNS algorithm specifically for water networks. We tested our new algorithm on four standard benchmark networks and a real-world WDN, including a case study of a large city. Our results demonstrate that the SA-VNS algorithm outperforms existing methods in terms of cost, speed, and overall effectiveness, making this research a significant advancement in both heuristic methods and parameter-tuning techniques for WDN optimization.
Successful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applie...
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ISBN:
(纸本)9781450328814
Successful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. The question whether a certain representation leads to better performing EAs than an alternative representation can only be answered when the operators applied are taken into consideration. The reverse is also true: deciding between alternative operators is only meaningful for a given *** EA practice one can distinguish two complementary approaches. The first approach uses indirect representations where a solution is encoded in a standard data structure, such as strings, vectors, or discrete permutations, and standard off-the-shelf search operators are applied to these genotypes. This is for example the case in standard genetic algorithms, evolution strategies, and some genetic programming approaches like grammatical evolution or cartesian genetic programming. To evaluate the solution, the genotype needs to be mapped to the phenotype space. The proper choice of this genotype-phenotype mapping is important for the performance of the EA search process. The second approach, the direct representation, encodes solutions to the problem in its most 'natural' space and designs search operators to operate on this *** in the last few years has identified a number of key concepts to analyse the influence of representation-operator combinations on EA performance. Relevant properties of representations are locality and *** is a result of the interplay between the search operator and the genotype-phenotype mapping. Representations are redundant if the number of phenotypes exceeds the number of possible genotypes. Redundant representations can lead to biased encodings if some phenotypes are on average represented by a larger number of genotyp
We evaluate the performance of panmictic evolutionary algorithms (EAs) in Byzantine environments, where fitness values are unreliable due to the potential presence of malicious agents. We investigate the impact of thi...
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ISBN:
(纸本)9783031407246;9783031407253
We evaluate the performance of panmictic evolutionary algorithms (EAs) in Byzantine environments, where fitness values are unreliable due to the potential presence of malicious agents. We investigate the impact of this phenomenon on the performance of the algorithm considering two different models of malicious behavior of different severity, taking the unreliability rate of the environment as a control parameter. We observe how there can be a significant toll in the quality of the results as the prevalence of cheating behavior increases, even for simple functions. Subsequently, we endow the EA with mechanisms based on redundant computation to cope with this issue, and examine their effectiveness. Our findings indicate that while a mechanism based on statistical averaging can be an effective approach under a relatively benign fault model, more hostile environments are better tackled via an approach based on majority voting.
Data warehouse plays a pivotal role in devising business strategies for organizations to stay competitive in the market. Analytical queries posed in the data warehouse must be answered efficiently. Using materialized ...
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Data warehouse plays a pivotal role in devising business strategies for organizations to stay competitive in the market. Analytical queries posed in the data warehouse must be answered efficiently. Using materialized views can be an effective strategy for reducing the response time of certain queries. Although materializing all potential views could enhance query performance, but this approach is impractical due to limited storage capacity. Moreover, choosing the optimal views is a problem of the NP-complete class. Consequently, a subset of views must be chosen to minimize query response time while adhering to storage constraints. This paper addresses this challenge by proposing a view selection algorithm that employs the reference-point-based non-dominated sorting algorithm (NSGA-III) to select views from a multi-dimensional lattice. The proposed algorithm is compared with the existing multi-objective view selection algorithms.
The use of evolutionary algorithms to solve constrained multi-objective optimization problems (CMOPs) with various characteristics and difficulties obtains considerable attention. Most of existing methods tend to intr...
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Chess Rating System for evolutionary algorithms (CRS4EAs) is a novel method for comparing evolutionary algorithms which evaluates and ranks algorithms regarding the formula from the Glicko-2 chess rating system. It wa...
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Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have gained widespread attention in dealing with them. As the objective d...
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Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have gained widespread attention in dealing with them. As the objective dimension increases, the error of predicting solutions based on surrogate models accumulates. Existing algorithms do not have strong selection pressure in the candidate solution obtaining and adaptive sampling stages. These make the effectiveness and area of application of the algorithms unsatisfactory. Therefore, this paper proposes a two-risk archive algorithm, which contains a strategy for mining high-risk and low-risk archives and a four-state adaptive sampling criterion. In the candidate solution mining stage, two types of Kriging models are trained, then conservative optimization models and non-conservative optimization models are constructed for model searching, followed by archive selection to obtain more reliable two-risk archives. In the adaptive sampling stage, in order to improve the performance of the algorithms, the proposed criterion considers environmental assessment, demand assessment, and sampling, where the sampling approach involves the improvement of the comprehensive performance in reliable environments, convergence and diversity in controversial environments, and surrogate model uncertainty. Experimental results on numerous benchmark problems show that the proposed algorithm is far superior to seven state-of-the-art algorithms in terms of comprehensive performance.
Dealing with traffic management for complex crossroads is a challenging problem for traffic control planners. As a contribution to solve this problem, the present paper develops a mesoscopic simulation model for detec...
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The problem of wildfire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the ...
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The problem of wildfire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). This uncertainty in the environmental values has led to the development of computational methods that search the space of possible combinations of parameters (also called scenarios) in order to obtain better predictions. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide this search. Moreover, the resulting predictions are based on a combination of multiple solutions from the space of scenarios. These methods have improved the quality of classical predictions;however, they have some limitations, such as premature convergence. In this work, we evaluate a new proposal for the optimization of scenarios that follows the Novelty Search paradigm. Novelty-based algorithms replace the objective function by a measure of the novelty of the solutions, which allows the search to generate solutions that are novel (in their behavior space) with respect to previously evaluated solutions. This approach avoids local optima and maximizes exploration. Our method, evolutionary Statistical System based on Novelty Search (ESS-NS), outperforms the quality obtained by its competitors in our experiments. Execution times are faster than other methods for almost all cases. Lastly, several lines of future work are provided in order to significantly improve these results.
Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene se...
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Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene selection, formulated as an optimization problem, this study introduces a new simple, efficient, and biologically plausible solution procedure where the collective power of the targeted gene set to discriminate between diseased and normal gene expression profiles was focused. It uses Simulated Annealing to solve the underlying optimization problem and termed here as Differential Gene Expression Based Simulated Annealing (DGESA). The Ranked Variance (RV) method has been applied to prioritize genes to form reference set to compare with the outcome of DGESA. In a case study on Eosinophilic Esophagitis (EoE) and other gastrointestinal diseases, RV identified the top 40 high-variance genes, overlapping with disease-causing genes from DGESA. DGESA identified 40 gene pathways each for EoE, Crohn's Disease (CD), and Ulcerative Colitis (UC), with 10 genes for EoE, 8 for CD, and 7 for UC confirmed in literature. For EoE, confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D, and SPRR2E. For CD, validated genes are NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. For UC, confirmed genes include TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9. RV and DGESA effectively elucidate molecular signatures in gastrointestinal diseases. Validating genes like SPRR2B, SPRR2D, SPRR2E, and STAT6 for EoE demonstrates DGESA's efficacy, highlighting potential targets for future research.
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