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Exploiting Linkage Information and problem-specific knowledge in Evolutionary Distribution Network Expansion Planning

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EVOLUTIONARY COMPUTATION 2018年第3期26卷 471-505页

作者： Ngoc Hoang Luong La Poutre, Han Bosman, Peter A. N. Ctr Wiskunde & Informat NL-1098 XG Amsterdam Netherlands Delft Univ Technol NL-2628 CD Delft Netherlands

This article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the estimation-of-distribution algorithm (EDA), and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). Not fully knowing the structure of the problem, we study the effect of linkage learning through the use of three linkage models: univariate, marginal product, and linkage tree. We furthermore experiment with the impact of incorporating different levels of problem-specific knowledge in the variation operators. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions. In all EAs, the marginal product model and its linkage learning procedure have difficulty in capturing and exploiting the DNEP problem structure. GOMEA, especially when combined with the linkage tree structure, is found to have the most robust performance by far, even when an out-of-the-box variant is used that does not exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for power system expansion planning problems, EAs that have the ability to effectively model and efficiently exploit problem structures, such as GOMEA, should be given priority, especially in the case of black-box or grey-box optimization.

关键词： Power system capacity planning linkage learning variation operators problem-specific knowledge

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A Greedy Cooperative Co-Evolutionary Algorithm With problem-specific knowledge for Multiobjective Flowshop Group Scheduling problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第3期27卷 430-444页

作者： He, Xuan Pan, Quan-Ke Gao, Liang Wang, Ling Suganthan, Ponnuthurai Nagaratnam Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha Qatar Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ignored in the literature. This article considers the FSDGSP to minimize makespan, total flow time, and total energy consumption, simultaneously. After the problem-specific knowledge is extracted, a mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. Since the FSDGSP includes multiple coupled subproblems, a greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space in depth. Meanwhile, a random mutation operator and a greedy energy-saving strategy are employed to adjust the processing speeds of machines to obtain a potential nondominated solution. A large number of experimental results show that the proposed algorithm significantly outperforms the existing classic multiobjective optimization algorithms, which is due to the usage of problem-related knowledge.

关键词： Cooperative co-evolutionary algorithm (CCEA) energy efficient flowshop sequence-dependent group scheduling multiobjective optimization problem (MOP) problem-specific knowledge

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Increasing the discriminatory power of bounding models using problem-specific knowledge when viewing design as a sequential decision process

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第2期62卷 709-728页

作者： Ororbia, Maximilian E. Chhabra, Jaskanwal P. S. Warn, Gordon P. Miller, Simon W. Yukish, Michael A. Qiu, Tong Penn State Univ University Pk PA 16802 USA

A recent design paradigm seeks to overcome the challenges associated with broadly exploring a design space requiring computationally expensive model evaluations by formally viewing design as a sequential decision process (SDP). With the SDP, a set of computational models of increasing fidelity are used to sequentially evaluate and systematically eliminate inefficient design alternatives from further consideration. Key to the SDP are concept models that are of lower fidelity than the true function and are constructed in such a way that when used to evaluate a given design, they return two-sided limits that bound the precise value of the decision criteria, hence referred to as bounding models. Efficiency in the SDP is achieved by using such low-fidelity, inexpensive models, early in the design process to eliminate inefficient design alternatives from consideration after which a higher fidelity, more computationally expensive model, is executed, but only on those design alternatives that appear promising. In general, low-fidelity models trade off discriminatory power for computational complexity;however, it can be demonstrated that knowledge of the underlying physics and/or mathematics can be used to increase the discriminatory power of the lower fidelity models for a given computational cost. Increasing the discriminatory power of the bounding models directly translates into an increase in the efficiency of the SDP. This paper discusses and demonstrates how knowledge of the underlying physics and/or mathematics, otherwise referred to as "problem-specific knowledge," such as monotonicity and concavity can be used to increase the discriminatory power of the bounding models in the context of the SDP and for engineering designs characterized by demand and capacity relationships. Furthermore, the concept of constructing the bounding models to systematically defer decisions on a subset of design variables, for example for a subsystem, is demonstrated, while retaining the de

关键词： Sequential decision process Bounding models problem-specific knowledge Deferred decisions System design

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What makes a VRP solution good? The generation of problem-specific knowledge for heuristics

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COMPUTERS & OPERATIONS RESEARCH 2019年 106卷 280-288页

作者： Arnold, Florian Sorensen, Kenneth Univ Antwerp Dept Engn Management ANT OR Operat Res Grp Antwerp Belgium

Heuristics are the weapon of choice when it comes to solving complex combinatorial optimization problems. Even though a large amount of research focuses on tuning heuristics on a specific problem, little research has been done to investigate structural characteristics of the problem itself. We argue that knowledge about the structural characteristics that distinguish good from not-so-good solutions of a combinatorial optimization problem, can be instrumental in designing efficient heuristics. We develop a data-mining based approach that can generate such knowledge and apply it to the vehicle routing problem. We define several metrics to characterize both a VRP solution and a VRP instance, and generate and classify 192.000 solutions for various instances. With these metrics we are able to distinguish between optimal and non-optimal solutions with an accuracy of up to 90%. We discuss the most distinguishing characteristics of good VRP solutions, and show how the knowledge thus generated can be used to improve the performance of an existing heuristic. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Data mining Heuristics Vehicle routing problem problem-specific knowledge

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Exploiting Linkage Information and problem-specific knowledge in Evolutionary Distribution Network Expansion Planning 15

Exploiting Linkage Information and Problem-Specific Knowledg...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Ngoc Hoang Luong La Poutre, Han Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands

ISBN: (纸本)9781450334723

This paper tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. We compare two evolutionary algorithms (EAs) for optimizing expansion plans: the classic genetic algorithm (GA) with uniform crossover and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) that learns and exploits linkage information between problem variables. We study the impact of incorporating different levels of problem-specific knowledge in the variation operators as well as two constraint-handling techniques: constraint domination and repair mechanisms. Experiments show that the use of problem-specific' variation operators is far more important for the classic GA to find high-quality solutions to the DNEP problem. GOMEA is found to have far more robust performance even when an out-of-box variant is used that doesn't exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for real-world applications like DNEP, EAs that have the ability to model and exploit problem structures, such as GOMEAs and estimation-of-distribution algorithms, should be given priority, especially when problem-specific knowledge is not straightforward to exploit, e.g. in the case of black-box optimization.

关键词： Power System Capacity Planning Linkage Learning Variation Operators problem-specific knowledge

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An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Zhang, Zi-Qi Xu, Yan-Xuan Qian, Bin Hu, Rong Wu, Fang-Chun Wang, Ling Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no -wait flowshop group scheduling problem with sequence -dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm & sdot;(EEDA) with problem -specific knowledge. First, a mixed integer linear programming (MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem -specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two -stage heuristics are presented to construct a high -quality population with diversity. Fourth, a probability model with problem -specific knowledge and a family -based updating mechanism are developed to accumulate valuable pattern information from high -quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-theart algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

关键词： Flowshop group scheduling Distributed no-wait flowshop group scheduling Estimation of distribution algorithm problem-specific knowledge

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An effective water wave optimization algorithm with problem-specific knowledge for the distributed assembly blocking flow-shop scheduling problem

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knowledge-BASED SYSTEMS 2022年 243卷

作者： Zhao, Fuqing Shao, Dongqu Wang, Ling Xu, Tianpeng Zhu, Ningning Jonrinaldi Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Univ Andalas Dept Ind Engn Padang 25163 Indonesia

The distributed assembly blocking flow-shop scheduling problem (DABFSP), which is a promising area in modern supply chains and manufacturing systems, has attracted great attention from researchers and practitioners. However, minimizing the total tardiness in DABFSP has not captured much attention so far. For solving the DABFSP with the total tardiness criterion, a mixed integer linear programming method is utilized to model the problem, wherein the total tardiness during the production process and assembly process are optimized simultaneously. A constructive heuristic (KBNEH) and a water wave optimization algorithm with problem-specific knowledge (KWWO) are presented. KBNEH is designed by combining a new dispatching rule with an insertion-based improvement procedure to obtain solutions with high quality. In KWWO, effective technologies, such as the re-developed destruction-construction operator, four local search methods under the framework of the variable neighborhood search strategy (VNS), the path-relinking method are applied to improve the performance of the algorithm. Comprehensive numerical experiments based on 900 small-scale benchmark instances and 810 large-scale benchmark instances are conducted to evaluate the performance of the presented algorithm. The experimental results obtained by KWWO are 1 to 4 times better than those obtained by the other comparison algorithms, which demonstrate that the effectiveness of KWWO is superior to the compared state-of-the-art algorithms for the considered problem. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Distributed assembly Flow-shop scheduling problem Total tardiness Water wave optimization problem-specific knowledge

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Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D

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APPLIED SOFT COMPUTING 2012年第7期12卷 1847-1864页

作者： Konstantinidis, Andreas Yang, Kun Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Frederick Univ Dept Comp Sci & Engn CY-1036 Nicosia Cyprus Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

An energy-efficient Wireless Sensor Network (WSN) design often requires the decision of optimal locations (deployment) and power assignments of the sensors to be densely deployed in an area of interest. In the literature, no attempts have been made on optimizing both decision variables for maximizing the network coverage and lifetime objectives, while maintaining the connectivity constraint, at the same time. In this paper, the Dense Deployment and Power Assignment problem (d-DPAP) in Wireless Sensor Networks (WSNs) is defined, and a Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) hybridized with a problem-specific Generalized Subproblem-dependent Heuristic (GSH), is proposed. In our method, the d-DPAP is decomposed into a number of scalar subproblems. The subproblems are optimized in parallel, by using neighbourhood information and problem-specific knowledge. The proposed GSH probabilistically alternates between six d-DPAP specific strategies, which are designed based on various WSN concepts and according to the subproblems objective preferences. Simulation results have shown that the proposed hybrid problem-specific MOEA/D performs better than the general-purpose MOEA/D and NSGA-II in several WSN instances, providing a diverse set of high-quality near-optimal network designs to facilitate the decision making process. The behavior of the MOEA/D-GSH in the objective space is also discussed. (C) 2012 Published by Elsevier B.V.

关键词： Wireless sensor networks Energy efficiency Dense deployment Multi-objective optimization Evolutionary algorithms Decomposition Heuristics problem-specific knowledge

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Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D

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APPLIED SOFT COMPUTING 2011年第6期11卷 4117-4134页

关键词： Wireless sensor networks Energy efficiency Dense deployment Multi-objective optimization Evolutionary algorithms Decomposition Heuristics problem-specific knowledge

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Artifical bee colony algorithm using problem-specific neighborhood strategies for the tree t-spanner problem

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APPLIED SOFT COMPUTING 2018年 62卷 110-118页

作者： Singh, Kavita Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

A tree t-spanner is a spanning tree T in which the ratio of distance between every pair of vertices is at most t times their shortest distance in a connected graph, where t is a value called stretch factor of T. On a given connected, undirected, and weighted graph, this paper studies the tree t-spanner problem( Tree t-SP) that aims to find a spanning tree whose stretch factor is minimum amongst all spanning trees of the graph. Being a NP-Hard for any fixed t > 1, this problem is under-studied in the domain of metaheuristic techniques. In literature, only genetic algorithm has been proposed for this problem. This paper presents an artificial bee colony (ABC) algorithm for this problem, where ABC algorithm is a swarm intelligence technique inspired by intelligent foraging behavior of honey bees. Neighborhood strategies of ABC algorithm particularly employ problem-specific knowledge that makes ABC algorithm highly effective in searching high quality solutions in less computational time. Computational experiments on a large set of randomly generated graph instances exhibit superior performance of ABC algorithm over the existing genetic algorithm for the Tree t-SP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Tree spanner Weighted graph problem-specific knowledge Swarm intelligence Artificial bee colony algorithm

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