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Branch-and-bound algorithms for simple assembly line balancing problem

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第1-2期36卷 169-177页

作者： Liu, S. B. Ng, K. M. Ong, H. L. Natl Univ Singapore Dept Ind & Syst Engn Singapore Singapore

In this paper, exact algorithms for solving the simple assembly line balancing type I problem are presented. The proposed algorithms consist of a constructive and two destructive algorithms. Several well-known lower-bound computational methods are also applied in these algorithms. Computational experiments were carried out to test the performance of the proposed algorithms based on a set of benchmark problem instances. The computational results show that the algorithms proposed in this paper are efficient in solving the simple assembly line balancing benchmark problem instances. Moreover, a problem instance whose optimal solution had previously been unknown is solved by one of the proposed algorithms.

关键词： branch-and-bound assembly line balancing destructive algorithm

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An accelerating scheme for destructive parsimonious extreme learning machine

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NEUROCOMPUTING 2015年 167卷 671-687页

作者： Zhao, Yong-Ping Li, Bing Li, Ye-Bo Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China AVIC Aeroengine Control Res Inst Wuxi 214063 Peoples R China

Constructive and destructive parsimonious extreme learning machines (CP-ELM and DP-ELM) were recently proposed to sparsify ELM. In comparison with CP-ELM, DP-ELM owns the advantage in the number of hidden nodes, but it loses the edge with respect to the training time. Hence, in this paper an equivalent measure is proposed to accelerate DP-ELM (ADP-ELM). As a result, ADP-ELM not only keeps the same hidden nodes as DP-ELM but also needs less training time than CP-ELM, which is especially important for the training time sensitive scenarios. The similar idea is extended to regularized ELM (RELM), yielding ADP-RELM. ADP-RELM accelerates the training process of DP-RELM further, and it works better than CP-RELM in terms of the number of hidden nodes and the training time. In addition, the computational complexity of the proposed accelerating scheme is analyzed in theory. From reported results on ten benchmark data sets, the effectiveness and usefulness of the proposed accelerating scheme in this paper is confirmed experimentally. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Single-hidden layer feedforward network Extreme learning machine destructive algorithm Constructive algorithms Sparseness

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Parsimonious kernel extreme learning machine in primal via Cholesky factorization

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NEURAL NETWORKS 2016年 80卷 95-109页

作者： Zhao, Yong-Ping Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China

Recently, extreme learning machine (ELM) has become a popular topic in machine learning community. By replacing the so-called ELM feature mappings with the nonlinear mappings induced by kernel functions, two kernel ELMs, i.e., P-KELM and D-KELM, are obtained from primal and dual perspectives, respectively. Unfortunately, both P-KELM and D-KELM possess the dense solutions in direct proportion to the number of training data. To this end, a constructive algorithm for P-KELM (CCP-KELM) is first proposed by virtue of Cholesky factorization, in which the training data incurring the largest reductions on the objective function are recruited as significant vectors. To reduce its training cost further, PCCP-KELM is then obtained with the application of a probabilistic speedup scheme into CCP-KELM. Corresponding to CCP-KELM, a destructive P-KELM (CDP-KELM) is presented using a partial Cholesky factorization strategy, where the training data incurring the smallest reductions on the objective function after their removals are pruned from the current set of significant vectors. Finally, to verify the efficacy and feasibility of the proposed algorithms in this paper, experiments on both small and large benchmark data sets are investigated. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Extreme learning machine Kernel method Parsimoniousness Constructive algorithm destructive algorithm

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