Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm

作     者：Wang, Ling Fang, Chen Suganthan, Ponnuthurai Nagaratnam Liu, Min 

作者机构：Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2014年第41卷第5期

页      面：2496-2513页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Basic Research and Development Program of China [2013CB329503] National Science Foundation of China [61174189, 61025018] Doctoral Program Foundation of Institutions of Higher Education of China National Science and Technology Major Project of China [2011ZX02504-008] 

主　　题：Estimation of distribution algorithm Probability model System-level synthesis problem Project scheduling Image compression standard 

摘      要：In this paper, the system-level synthesis problem (SLSP) is modeled as a multi-objective mode-identity resource-constrained project scheduling problem with makespan and resource investment criteria (MOMIRCPSP-MS-RI). Then, a hybrid Pareto-archived estimation of distribution algorithm (HPAEDA) is presented to solve the MOMIRCPSP-MS-RI. To be specific, the individual of the population is encoded as the activity-mode-priority-resource list (AMPRL), and a hybrid probability model is used to predict the most promising search area, and a Pareto archive is used to preserve the non-dominated solutions that have been explored, and another archive is used to preserve the solutions for updating the probability model. Moreover, specific sampling mechanism and updating mechanism for the probability model are both provided to track the most promising search area via the EDA-based evolutionary search. Finally, the modeling methodology and the HPAEDA are tested by an example of a video codec based on the H.261 image compression standard. Simulation results and comparisons demonstrate the effectiveness of the modeling methodology and the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.