作者：
Yao, LeGe, ZhiqiangZhejiang Univ
Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China
In this paper, two enhanced binarydifferentialevolution (BDE) algorithms are proposed to select variables for nonlinear process soft sensor development. Firstly, the Parallel BDE (PBDE) algorithm is presented to ext...
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In this paper, two enhanced binarydifferentialevolution (BDE) algorithms are proposed to select variables for nonlinear process soft sensor development. Firstly, the Parallel BDE (PBDE) algorithm is presented to extract the optimal individuals of several parallel short evolution paths of basic BDE, where the spurious variables are effectively eliminated. And the most relevant variables are selected through a double-layer selection strategy with the validating Root Mean Square Error (RMSE) for evaluating criterion. Secondly, the Boosting BDE (BBDE) algorithm is proposed through applying the boosting technique to the parallel evolution paths. The performance of the previous path needs to be taken into account when conducting the current evolution path. The selected probabilities of variables are given through the weighted summation of the selection results of all paths. Also, a double-layer selection is conducted on BBDE algorithm. The feasibility and effectiveness of the proposed methods are demonstrated through a nonlinear numerical example and a real industrial process. (C) 2017 Elsevier Ltd. All rights reserved.
Although real-coded differentialevolution (DE) algorithm's can perform well on continuous optimization problems (CoOPs), designing an efficient binary-coded DE algorithm is still a challenging task. Inspired by t...
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Although real-coded differentialevolution (DE) algorithm's can perform well on continuous optimization problems (CoOPs), designing an efficient binary-coded DE algorithm is still a challenging task. Inspired by the learning mechanism in particle swarm optimization (PSO) algorithms, we propose a binary learning differentialevolution (BLDE) algorithm that can efficiently locate the global optimal solutions by learning from the last population. Then, we theoretically prove the global convergence of BLDE, and compare it with some existing binary-coded evolutionary algorithms (EAs) via numerical experiments. Numerical results show that BLDE is competitive with the compared EAs. Further study is performed via the change curves of a renewal metric and a refinement metric to investigate why BLDE cannot outperform some compared EM for several selected benchmark problems. Finally, we employ BLDE in solving the unit commitment problem (UCP) in power systems to show its applicability to practical problems. (C) 2014 Elsevier B.V. All rights reserved.
At present, the functional verification of a device represents the highest cost during manufacturing. To reduce that cost, several methods have been suggested. In this work we propose a method which produces a set of ...
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At present, the functional verification of a device represents the highest cost during manufacturing. To reduce that cost, several methods have been suggested. In this work we propose a method which produces a set of binary test sequences by means of a Compact binary differential evolution algorithm (Compact-BinDE). The strategy employed is based on the use of coverage models and cost functions in the verification process, which are built with relevant conditions or coverage points representing the full device behavior. The main problem is to cover all difficult situations since the relationships between the test points and the input data in the design are not trivial. The test generation method is included with a proposed verification platform based on a simulation representing a hybrid method. The main contribution of this work is that the method obtains test vector sequences that maximize the coverage percentage on run-time device simulation with an efficient search in the binary domain. Also, different to the previous works that used meta-heuristics, the proposed method by means of the Compact-BinDE algorithm can reduce the simulation time used to obtain test sequences that exercise the coverage points. The results show that the proposed method represents a good alternative to generate test sequences to cover the coverage points during the functional verification.
As an auxiliary facility, roadside units (RSUs) can well improve the shortcomings incurred by ad hoc networks and promote network performance in a vehicular ad hoc network (VANET). However, deploying a large number of...
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As an auxiliary facility, roadside units (RSUs) can well improve the shortcomings incurred by ad hoc networks and promote network performance in a vehicular ad hoc network (VANET). However, deploying a large number of RSUs will lead to high installation and maintenance costs. Therefore, trying to find the best locations is a key issue when deploying RSUs with the set delay and budget. In this paper, we study the delay-bounded and cost-limited RSU deployment (DBCL) problem in urban VANET. We prove it is non-deterministic polynomial-time hard (NP-hard), and a binarydifferentialevolution scheme is proposed to maximize the number of roads covered by deploying RSUs. Opposite-based learning is introduced to initialize the first generation, and a binarydifferential mutation operator is designed to obtain binary coding. A random variable is added to the traditional crossover operator to increase population diversity. Also, a greedy-based individual reparation and promotion algorithm is adopted to repair infeasible solutions violating given constraints, and to gain optimal feasible solutions with the compromise of given limits. Moreover, after selection, a solution promotion algorithm is executed to promote the best solution found in generation. Simulation is performed on analog trajectories sets, and results show that our proposed algorithm has a higher road coverage ratio and lower packet loss compared with other schemes.
This study presents a new binary differential evolution algorithm for probabilistic AC/DC transmission expansion planning (TEP). The probabilistic model would consider the uncertainty of distant wind/solar energy reso...
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This study presents a new binary differential evolution algorithm for probabilistic AC/DC transmission expansion planning (TEP). The probabilistic model would consider the uncertainty of distant wind/solar energy resources as well as load demand. In many power systems, the necessity of considering the impacts of HVDC links on the TEP studies is inevitable. Therefore, in this study, these links are considered a substitute candidate for the conventional HVAC transmission lines. The stochastic modelling and integration of HVDC links for TEP problem have introduced new challenges to transmission system planners. To overcome these problems, an efficient fuzzy set-based optimisation technique is used to solve the multi-objective TEP problem. Finally, the proposed method is applied to the test system consisting of 24 buses.
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