Genetic algorithms (GAs) have proved their efficiency solving many complex optimization problems. GAs can be also applied for "black-box" problems, because they realize the "blind" search and do no...
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Genetic algorithms (GAs) have proved their efficiency solving many complex optimization problems. GAs can be also applied for "black-box" problems, because they realize the "blind" search and do not require any specific information about features of search space and objectives. It is clear that a GA uses the "Trial-and-Error" strategy to explorer search space, and collects some statistical information that is stored in the form of genes in the population. Estimation of Distribution algorithms (EDA) have very similar realization as GAs, but use an explicit representation of search experience in the form of the statistical probabilities distribution. In this study we discus some approaches for improving the standard GA performance by combining the binary GA with EDA. Finally, a novel approach for the largescale global optimization is proposed. The experimental results and comparison with some well-studied techniques are presented and discussed.
We presenta multiple ant-colonyalgorithm (MACA)for the graph bisection problem. The aim of this paper is to compare the performance of the MACA with results on the benchmark graphs from Graph partitioning Archive at t...
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ISBN:
(纸本)9616303619
We presenta multiple ant-colonyalgorithm (MACA)for the graph bisection problem. The aim of this paper is to compare the performance of the MACA with results on the benchmark graphs from Graph partitioning Archive at the University of Greenwich. Experimental results show that the MACA is comparable with the state-of-the-art graph bisection algorithms.
In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct t...
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In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct three-dimensional simulation of the workshop, thereby achieving dynamic scheduling and energy efficiency optimization of the workshop. The optimization of digital twin manufacturing systems has become a focus of research. In order to reduce power consumption and production time in manufacturing workshops, the study adopted a non-dominated sorting genetic algorithm to improve its elitist retention strategy for the problem of easily falling into local optima. On the ground of the idea of multi-objective optimization, the optimization was carried out with the production time and power consumption of the manufacturing industry as the objectives. The experiment showcased that the improved algorithm outperforms the multi-objective optimization algorithm on the ground of decomposition and the evolutionary algorithm on the ground of Pareto dominance. Compared to the two comparison algorithms, the production time optimization effect and power consumption optimization effect of different numbers of devices were 11.12%-21.37% and 2.14%-6.89% higher, respectively. The optimization time of the improved algorithm was 713.5 seconds, that was reduced by 173.8 seconds and 179.8 seconds compared to the other two algorithms, respectively. The total power consumption of the improved optimization model was 2883.7kWs, which was reduced by 32.0kW*s and 45.5kW*s compared to the other two algorithms, respectively. This study proposed a new multi-objective optimization algorithm for the current digital twin manufacturing industry. This algorithm effectively reduces production time and power consumption, and has important guiding significance for manufacturing system optimization in actual production environments.
The proceedings contain 31 papers. The topics discussed include: swish function based LMS algorithm with variable step size;research on the comprehensive performance evaluation method of genetic algorithm based on Euc...
ISBN:
(纸本)9798331528881
The proceedings contain 31 papers. The topics discussed include: swish function based LMS algorithm with variable step size;research on the comprehensive performance evaluation method of genetic algorithm based on Euclidean distance minimization;optimization design of regional coverage remote sensing constellation based on improved particle swarm optimization algorithm;a domain-level transformation scenario classification algorithm based on multilevel feature representation;research of continuous thermoplastic coating equipment based on virtual simulation;and the mode of speed control and safety discussion of the permanent magnet synchronous elevator based on fuzzy logic control.
The CNN literature contains several papers about how to use the genetic algorithms for template tuning. This paper shows a possible CNN-UM implementation of the control algorithm of the genetic algorithm in a special ...
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ISBN:
(纸本)9781424406395
The CNN literature contains several papers about how to use the genetic algorithms for template tuning. This paper shows a possible CNN-UM implementation of the control algorithm of the genetic algorithm in a special fine-grained parallel version. The interesting of the analogic mapping is the different set of operators, which can be evaluated fast and efficient on the CNN-UM platform. The rational behind the spatial implementation is the possibility of focal-plane optimization.
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-opt...
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ISBN:
(纸本)9781479919635
We consider non-differentiable convex optimization problems that vary continuously in time and we propose algorithms that sample these problems at specific time instances and generate a sequence of converging near-optimal decision variables. This sequence converges up to a bounded error to the solution trajectory of the time-varying non-differentiable problems. We illustrate through analytical examples and a realistic numerical simulation the benefit of the algorithms in signal processing applications, e.g., for reconstructing time-varying sparse signals.
The focus of this paper is to analyze the supply chain routes by means of artificial intelligence techniques for reducing transportation costs. The simulation model, built in eM-Plant, is used to implement two differe...
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ISBN:
(纸本)9781424413478
The focus of this paper is to analyze the supply chain routes by means of artificial intelligence techniques for reducing transportation costs. The simulation model, built in eM-Plant, is used to implement two different approaches based on the ants theory and the genetic algorithms. A comparison of results is made in order to identify, the better approach to adopt for the optimization process.
In this paper, a systematic optimization approach for clustering proximity or similarity data is developed. Starting from Fundamental invariance and robustness properties, a set of axioms is proposed and discussed to ...
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In this paper, a systematic optimization approach for clustering proximity or similarity data is developed. Starting from Fundamental invariance and robustness properties, a set of axioms is proposed and discussed to distinguish different cluster compactness and separation criteria. The approach covers the case of sparse proximity matrices, and is extended to nested partitionings for hierarchical data clustering. To solve the associated optimization problems, a rigorous mathematical framework for deterministic annealing and mean-field approximation is presented. Efficient optimization heuristics are derived in a canonical way, which also clarifies the relation to stochastic optimization by Gibbs sampling. Similarity-based clustering techniques have a broad range of possible applications in computer vision, pattern recognition, and data analysis. As a major practical application we present a novel approach to the problem of unsupervised texture segmentation, which relies on statistical tests as a measure of homogeneity. The quality of the algorithms is empirically evaluated on a large collection of Brodatz-like micro-texture Mondrians and on a set of real-word images. To demonstrate the broad usefulness of the theory of proximity based clustering the performances of different criteria and algorithms are compared on an information retrieval task for a document database. The superiority of optimizationalgorithms for clustering is supported by extensive experiments. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
In this paper we investigate the computational complexity of a combinatorial problem that arises in the reverse engineering of protein and gene networks. Our contributions are as follows: We abstract a combinatorial v...
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In this paper we investigate the computational complexity of a combinatorial problem that arises in the reverse engineering of protein and gene networks. Our contributions are as follows: We abstract a combinatorial version of the problem and observe that this is "equivalent" to the set multicover problem when the "coverage" factor k is a function of the number of elements it of the universe. An important special case for our application is the case in which k = n - 1. We observe that the standard greedy algorithm produces an approximation ratio of Omega(log n) even if k is "large" i.e k = n - c for some constant c > 0. Let 1 < a < n denote the maximum number of elements in any given set in our set multicover problem. Then, we show that a non-trivial analysis of a simple randomized polynomial-time approximation algorithm for this problem yields an expected approximation ratio E vertical bar r(a, k)vertical bar that is an increasing function of a/k. The behavior of E vertical bar r(a, k)vertical bar is roughly as follows: it is about In(a/k) when a/k is at least about e(2) approximate to 7.39, and for smaller values of a/k it decreases towards 1 as a linear function of root a/k with lim(a/k -> 0) E vertical bar r(a, k)vertical bar = 1. Our randomized algorithm is a cascade of a deterministic and a randomized rounding step parameterized by a quantity beta followed by a greedy solution for the remaining problem. We also comment about the impossibility of a significantly faster convergence of E vertical bar r(a, k)vertical bar towards 1 for any polynomial-time approximation algorithm. (c) 2006 Elsevier B.V. All rights reserved.
This paper discusses the use of genetic algorithms (GA) in a perimeter security CAD system (PSCAD). In this paper we compare the performance of a PSCAD system that uses exhaustive search for optimization with one that...
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ISBN:
(纸本)9781424413478
This paper discusses the use of genetic algorithms (GA) in a perimeter security CAD system (PSCAD). In this paper we compare the performance of a PSCAD system that uses exhaustive search for optimization with one that uses a genetic algorithm.
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