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IEEE AUTOTESTCON Conference

作者： Deng Libao Wei Debao Qiao Liyan Bian Xiaolong Zhang Baoquan Harbin Inst Technol Weihai Sch Informat & Elect Engn Weihai Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin Peoples R China

ISBN: (纸本)9781509007899

System on a chip (SOC) based on reusable IP core has been widely used in integrated circuit (IC) design and manufacturing. However SOC efficient test is still the bottle-neck issue. Test scheduling can enhance parallel test to minimize test application time at SOC system level. We present a modified differential evolution (MDE) approach to solve the problems of test scheduling and test access mechanism (TAM) partition for system on chips. A new hybrid mutation mechanism based on the probability estimation operator is introduced in the paper for better handling test scheduling, where the first variation coefficient stays the same but the second variation coefficient changes with iterative times. The different evolutionary strategies are adopted to accelerate effectively convergence without loss in population diversity as far as possible. Different mutation operators and evolutionary strategies are combined to get optimal results. The experimental results on ITC'02 SOC benchmarks are encouraging compared with the improved quantum-inspired evolutionary (IQI), genetic algorithm (GA), the integer linear programming formulation (ILP) and heuristic approaches.

关键词： SOC test scheduling differential evolution hybrid mutation mechanism probability estimation operator

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A novel modified binary differential evolution algorithm and its applications

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NEUROCOMPUTING 2012年 98卷 55-75页

作者： Wang, Ling Fu, Xiping Mao, Yunfei Menhas, Muhammad Ilyas Fei, Minrui Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

Differential Evolution (DE) is a simple yet efficient global optimization algorithm. However, the standard DE and most of its variants operate in the continuous space, which cannot solve the binary-coded optimization problems directly. To tackle this problem, this paper proposes a novel modified binary differential evolution algorithm (NMBDE) inspired by the concept of estimation of Distribution Algorithm and DE. A novel probability estimation operator is developed for NMBDE, which can efficiently maintain diversity of population and achieve a better tradeoff between the exploration and exploitation capabilities by cooperating with the selection operator. Furthermore, the parameter study of NMBDE is run and the analysis is performed to improve the global search ability and scalability of algorithm. The effectiveness and efficiency of NMBDE was verified in applications to the numerical optimization and multidimensional knapsack problems. The experimental results demonstrate that NMBDE has the better global search ability and outperforms the discrete binary DE, the modified binary DE, the discrete binary Particle Swarm Optimization and the binary Ant System in terms of accuracy and convergence speed. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Differential evolution Binary encoding probability estimation operator Numerical optimization Multidimensional knapsack problem

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A Modified Binary Differential Evolution Algorithm

A Modified Binary Differential Evolution Algorithm

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International Conference on Life System Modeling and Simulation / International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS-ICSEE)

作者： Wang, Ling Fu, Xiping Menhas, Muhammad Ilyas Fei, Minrui Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China

ISBN: (纸本)9783642155963

Differential evolution (DE) is a simple, yet efficient global optimization algorithm. As the standard DE and most of its variants operate in the continuous space, this paper presents a modified binary differential evolution algorithm (MBDE) to tackle the binary-coded optimization problems. A novel probability estimation operator inspired by the concept of distribution of estimation algorithm is developed, which enables MBDE to manipulate binary-valued solutions directly and provides better tradeoff between exploration and exploitation cooperated with the other operators of DE. The effectiveness and efficiency of MBDE is verified in application to numerical optimization problems. The experimental results demonstrate that MBDE outperforms the discrete binary DE, the discrete binary particle swarm optimization and the binary ant system in terms of both accuracy and convergence speed on the suite of benchmark functions.

关键词： Differential Evolution Binary Encoding probability estimation operator Multidimensional Knapsack Problem

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