Doubly-stochastic matrices play a vital role in modern applications of complex networks such as tracking and decentralized state estimation, coordination and control of autonomous agents. A central theme in all of the...
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Doubly-stochastic matrices play a vital role in modern applications of complex networks such as tracking and decentralized state estimation, coordination and control of autonomous agents. A central theme in all of the above is consensus, that is, nodes reaching agreement about the value of an underlying variable (e.g. the state of the environment). Despite the fact that complex networks have been studied thoroughly, the communication graphs are usually described by symmetric matrices due to their advantageous theoretical properties. We do not yet have methods for optimizing generic doubly-stochastic matrices. In this paper, we propose a novel formulation and framework, EvoDSM, for achieving fast linear distributed averaging by: (a) optimizing the weights of a fixed graph topology, and (b) optimizing for the topology itself. We are concerned with graphs that can be described by positive doubly-stochastic matrices. Our method relies on swarm and evolutionary optimization algorithms and our experimental results and analysis showcase that our method (1) achieves comparable performance with traditional methods for symmetric graphs, (2) is applicable to non-symmetric network structures and edge weights, and (3) is scalable and can operate effectively with moderately large graphs without engineering overhead.
The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both struct...
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The assessment of apple quality is pivotal in agricultural production management, and apple ripeness is a key determinant of apple quality. This paper proposes an approach for assessing apple ripeness from both structured and unstructured observation data, i.e., text and images. For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. For unstructured image data, an Enhanced-YOLOv8+, a modified YOLOv8 architecture integrating Detect Efficient Head (DEH) and Efficient Channel Attention (ECA) mechanism, was employed for precise apple localization and ripeness identification. The synergistic application of these methods resulted in a significant improvement in prediction accuracy. These approaches provide a robust framework for apple quality assessment and deepen the understanding of the relationship between apple maturity and observed indicators, facilitating more informed decision-making in postharvest management.
Quantum error correction and the use of quantum error correction codes are likely to be essential for the realization of practical quantum computing. Because the error models of quantum devices vary widely, quantum co...
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Quantum error correction and the use of quantum error correction codes are likely to be essential for the realization of practical quantum computing. Because the error models of quantum devices vary widely, quantum codes that are tailored for a particular error model may have much better performance. In this work, we present a novel evolutionary algorithm that searches for an optimal stabilizer code for a given error model, number of physical qubits, and number of encoded qubits. We demonstrate an efficient representation of stabilizer codes as binary strings, which allows for random generation of valid stabilizer codes as well as mutation and crossing of codes. Our algorithm finds stabilizer codes whose distance closely matches the best-known-distance codes of Grassl (2007) for n <= 20 physical qubits. We perform a search for optimal distance Calderbank-Steane-Shor codes and compare their distance to the best known codes. Finally, we show that the algorithm can be used to optimize stabilizer codes for biased error models, demonstrating a significant improvement in the undetectable error rate for [[12,1]](2) codes versus the best-known-distance code with the same parameters. As part of this work, we also introduce an evolutionary algorithm QDistEvol for finding the distance of quantum error correction codes.
Feature selection is a critical preprocessing task in machine learning, particularly with high-dimensional datasets and decision-making while handling big data which presents significant challenges. This paper introdu...
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This paper presents an in-depth exploration of hybrid soft computing models that integrate fuzzy logic, neural networks, and evolutionary algorithms. These hybrid models offer a powerful approach to solving complex re...
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Strategic spatial planning is becoming more popular around the world as a decision-making way to build a unified vision for directing the medium- to long-term development of land/marine areas. Recently, the study of m...
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Strategic spatial planning is becoming more popular around the world as a decision-making way to build a unified vision for directing the medium- to long-term development of land/marine areas. Recently, the study of marine areas in terms of spatial planning such as Marine Spatial Planning (MSP) has received much attention. One of the challenging issues in MSP is to make a balance between determining the ideal zone for a new activity while also considering the locations of existing activities. This spatial zoning problem for multi-uses with multiple objectives could be formulated as optimization models. This paper presents and compares the results of two multi-objective evolutionary-based algorithms (MOEAs), Synchronous Hypervolume-based non-dominated sorting genetic algorithm-II (SH-NSGA-II) which is an extension of NSGA-II and a memetic algorithm (MA) in which SH-NSGA-II is enhanced with a local search. These proposed algorithms are used to solve the multi-objective spatial zoning optimization problem, which seeks to maximize the zone interest value assigned to the new activity while simultaneously maximizing its spatial compactness. We introduce several innovations in these proposed algorithms to address the problem constraints and to improve the robustness of the traditional NSGA-II and MA approaches. Unlike traditional ones, a different stop condition, multiple crossover, mutation, and repairing operators, and also a local search operator are developed. A comparative study is presented between the results obtained using both algorithms. To guarantee robust results for both algorithms, their parameters are calibrated and tuned using the Multi-Response Surface Methodology (MRSM) method. The effective and non-effective components, as well as the validity of the regression models, are determined using analysis of variance (ANOVA). Although SH-NSGA-II has revealed a good efficiency, its performance is still improved using a local search scheme within SH-NSGA-II, whic
We propose novel evolutionary algorithms for solving single- and multi -objective political redistricting problems. The objectives include population equality, compactness of districts, deviation from the current dist...
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We propose novel evolutionary algorithms for solving single- and multi -objective political redistricting problems. The objectives include population equality, compactness of districts, deviation from the current districting, and an expected number of mandates attainable by some parties. The former two ensure the constructed solutions are reasonable, while the latter pair is meaningful for the post -analysis on how the alternation of existing districts may affect election outcomes. We operate on data concerning geography, demography, and politics in Poland. The experiments reveal that our algorithms efficiently handle the fourobjective variant of the problem. In a single test run, we evaluate around one million solutions in nearly two hours on an average class computer, which is satisfactory given the problem's complexity. The methods construct high -quality non -dominated solutions, outperforming the current districting and revealing the tradeoffs between the objectives. The post -analysis allows us to observe connections between the expected number of mandates and the remaining three objectives. Specifically, attaining a greater number of mandates requires more significant changes in delineating the districts and potential violations of constraints. We also exhibit that the space for possible political manipulations increases when more districts can be determined.
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