In order to take advantages of evolutionary algorithms inspired by different biological evolutions, varieties of approaches have been proposed to combine them together. One of them is the portfolio approach, which kee...
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ISBN:
(纸本)9781509006229
In order to take advantages of evolutionary algorithms inspired by different biological evolutions, varieties of approaches have been proposed to combine them together. One of them is the portfolio approach, which keeps choosing a component algorithm from a portfolio of evolutionary algorithms (EAs) to run during the optimizing process. In our approach, each component algorithm has its own population and runs independently without information exchange. At the beginning of each generation, only the component algorithm with the best predicted performance is allowed to run. The proposed portfolio approach is tested on the CEC2016 real-parameter single objective optimization benchmarks. The results show that it is competitive.
Experimental tests are probably the most direct way of evaluating the performance of computer algorithms and of appealing to their users. However, there seems to be no apparent agreement regarding how to generate samp...
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This paper proposes a new neural fusion algorithm for fast robust image restoration without requiring the optimal regularization parameter. The new neural fusion algorithm is based on a new reduced dimension neural ne...
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ISBN:
(纸本)9781509037100
This paper proposes a new neural fusion algorithm for fast robust image restoration without requiring the optimal regularization parameter. The new neural fusion algorithm is based on a new reduced dimension neural network (RDNN). The RDNN is guaranteed to obtain an optimal fusion weight. The proposed RDNN-based neural fusion algorithm uses only a very small solution space to compute the optimal fusion weight, unlike existing neural fusion algorithms with solution space dimension being grater than image size. Unlike current image restoration algorithms, the proposed neural fusion algorithm has a low-dimensional solution space Computed results show that the proposed new algorithm has a robust performance against non-Gaussian noise and can obtain a good image estimate at a fast speed by using the non-optimal regularization parameter.
This article tackles the problem of natural gas sensors allocation in oil platforms. A mathematical formulation of the problem is developed and used in the creation of a genetic algorithm whose goals are to maximize t...
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Effective Butterfly Optimizer(EBO) is a self-adaptive Butterfly Optimizer which incorporates a crossover operator in Perching and Patrolling to increase the diversity of the population. This paper proposes a new retre...
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ISBN:
(纸本)9781509046010
Effective Butterfly Optimizer(EBO) is a self-adaptive Butterfly Optimizer which incorporates a crossover operator in Perching and Patrolling to increase the diversity of the population. This paper proposes a new retreat phase called Covariance Matrix Adapted Retreat Phase (CMAR), which uses covariance matrix to generate a new solution and thus improves the local search capability of EBO. This version of EBO is called EBOwithCMAR. We evaluated the performance of EBOwithCMAR on CEC-2017 benchmark problems and compared with the results of winners of a special session of CEC-2016 for bound-constrained problems. The experimental results show that EBOwithCMAR is competitive with the compared algorithms.
SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics. Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities. ...
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ISBN:
(纸本)9781509020201
SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics. Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities. However, the existing SimRank computationalgorithms either overlook uncertainty in graph structures or is based on an unreasonable assumption (Du et al). In this paper, we study SimRank similarities on uncertain graphs based on the possible world model of uncertain graphs. Following the random-walk-based formulation of SimRank on deterministic graphs and the possible worlds model of uncertain graphs, we define random walks on uncertain graphs for the first time and show that our definition of random walks satisfies Markov's property. We formulate the SimRank measure based on random walks on uncertain graphs. We discover a critical difference between random walks on uncertain graphs and random walks on deterministic graphs, which makes all existing SimRank computationalgorithms on deterministic graphs inapplicable to uncertain graphs. To efficiently compute SimRank similarities, we propose three algorithms, namely the baseline algorithm with high accuracy, the sampling algorithm with high efficiency, and the two-phase algorithm with comparable efficiency as the sampling algorithm and about an order of magnitude smaller relative error than the sampling algorithm. T he extensive experiments and case studies verify the effectiveness of our SimRank measure and the efficiency of our SimRank computationalgorithms.
This paper presents an analysis of fractal microstrip patch antenna using new wave iterative computation (WIC) that is a full electromagnetic wave analysis. The wave iterative simulation based on wave propagation and ...
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This paper presents an analysis of fractal microstrip patch antenna using new wave iterative computation (WIC) that is a full electromagnetic wave analysis. The wave iterative simulation based on wave propagation and iterative method is created on graphical user interface (GUI) of MATLAB software. The proposed fractal microstrip patch antenna is simulated by developed simulation program and measurement using network analyzer. The analyzed result using wave iterative computation was successfully in comparing with results of measurement and classical simulation tools. (C) 2016 The Authors. Published by Elsevier B.V.
Two algorithms for determining of ICT time instrumental function are proposed that are applicable for the optical and IR regions of recorded radiation at different modes of operation. A program based on the algorithms...
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Harris hawks optimization (HHO) is one of the newest metaheuristic algorithms (MHAs) which mimic the interdependent behaviour and hunting style of Harris hawks in nature. It is an efficient swarm optimization techniqu...
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Harris hawks optimization (HHO) is one of the newest metaheuristic algorithms (MHAs) which mimic the interdependent behaviour and hunting style of Harris hawks in nature. It is an efficient swarm optimization technique that has been used to solve various kinds of optimization problems. However, for some optimization cases, it has a tendency to be trapped into local search space and it endures an improper balance between exploitation and exploration. To get rid of this situation and to explore the global searching ability of HHO, an effective hybrid method improved teaching-learning HHO (ITLHHO) has been developed using improved teaching-learning-based optimization for solving different kinds of engineering design and numerical optimization problems. The performance of ITLHHO has been demonstrated by 33 well-known benchmark functions, including IEEE congress of Evolutionary computation Benchmark Test Functions (CEC-C06, 2019 Competition) and 10 multidisciplinary challenging engineering optimization problems. After illustration, the outcomes of the proposed ITLHHO are compared with several recently developed competitive MHAs. Additionally, the ITLHHO results are statistically investigated with the Wilcoxon rank-sum test and multiple comparison test to show the significance of the results. The experimental results suggest that ITLHHO significantly outperforms other algorithms and becomes a remarkable and promising tool for solving various kinds of optimization problems.
The envelope polygon of a set of lines, L, is the polygon consisting of the finite length segments that hound the infinite faces of the arrangement A(L). Given an envelope polygon, we show how to sort its edges by slo...
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