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A General Variable Neighborhood Search approach based on a p-median model for cellular manufacturing problems

OPTIMIZATION LETTERS 2022年第1期16卷 137-151页

作者： Ibrahim, Saber Jarboui, Bassem Qassim Univ Coll Business & Econ Dept Management Informat Syst & Prod Management POB 6640 Buraydah 51452 Saudi Arabia Higher Coll Technol Dept Ind Management Abu Dhabi U Arab Emirates

One of the practical application in cellular manufacturing systems is the cell formation problem (CFP). Its main idea is to group machines into cells and parts into part families in a way that the number of exceptional elements and the number of voids are minimized. In literature, it is proved that p-median is an efficient mathematical programming model for solving CF problems. In the present work, we develop a modified p-median based model dedicated to solve CFP respecting the objective of minimizing the sum of dissimilarities of machines. For this aim, we applied a General Variable Neighborhood Search algorithm and we collaborated it with an estimation of distribution algorithm maximizing the group capability index and the grouping efficacy evaluation criteria. Thirty CF problems are taken from the literature and tested by our proposed algorithm and the experimental study demonstrated that the proposed method guided by p-median model provides high quality cells in speed running times and beats other state-of-the-art algorithms particularly for CF instances with large sizes.

关键词： Cell formation problem p-Median model General Variable Neighborhood Search estimation of distribution algorithm Grouping efficacy Group capability index

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Experimental Comparison of Six Population-Based algorithms for Continuous Black Box Optimization

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EVOLUTIONARY COMPUTATION 2012年第4期20卷 483-508页

作者： Posik, Petr Kubalik, Jiri Czech Tech Univ Fac Elect Engn CR-16635 Prague Czech Republic

Six population-based methods for real-valued black box optimization are thoroughly compared in this article. One of them, Nelder-Mead simplex search, is rather old, but still a popular technique of direct search. The remaining five (POEMS, G3PCX, Cauchy EDA, BIPOP-CMA-ES, and CMA-ES) are more recent and came from the evolutionary computation community. The recently proposed "comparing continuous optimizers" (COCO) methodology was adopted as the basis for the comparison. The results show that BIPOP-CMA-ES reaches the highest success rates and is often also quite fast. The results of the remaining algorithms are mixed, but Cauchy EDA and POEMS are usually slow.

关键词： Benchmarking real-valued black box optimization population-based algorithms estimation of distribution algorithm evolutionary strategy covariance matrix adaptation

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Multi-objective search-based software modularization: structural and non-structural features

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SOFT COMPUTING 2019年第21期23卷 11141-11165页

作者： Jalali, Nafiseh Sadat Izadkhah, Habib Lotfi, Shahriar Univ Tabriz Fac Math Sci Dept Comp Sci Tabriz Iran

Software modularization techniques are employed to understand a software system. The purpose of modularization is to decompose a software system from a source code into meaningful and understandable subsystems (modules). Since modularization of a software system is an NP-hard problem, the modularization quality obtained using evolutionary algorithms is more reasonable than greedy algorithms. All evolutionary algorithms presented for software modularization only consider structural features that are dependent on the syntax of programming languages. For most programming languages, does not exist a tool to extract structural features, so it is not possible to modularize them. To overcome this problem, this paper presents a new multi-objective fitness function, named MOF, which exploits the structural (such as calling dependency and inheritance dependency) and non-structural features (such as semantic contained in the code comments and identifier names), aiming to automatically guide optimization algorithms to find a good decomposition of software systems. To evaluate the performance of this objective function, three optimization strategies, namely global-based search, combining global and local search, and estimation of distribution (EoD), are adapted to optimize it. The results on Mozilla Firefox indicate that the proposed algorithm which is based on EoD along with the new MOF function outperforms the algorithms that use structural-based objective functions in guiding the optimization process, in finding more understandable modules.

关键词： Evolutionary algorithms Modularization Clustering estimation of distribution algorithm Reverse Engineering

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Improved RM-MEDA with local learning

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SOFT COMPUTING 2014年第7期18卷 1383-1397页

作者： Li, Yangyang Xu, Xia Li, Peidao Jiao, Licheng Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

In this paper, local learning is proposed to improve the speed and the accuracy of convergence performance of regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA), a typical multi-objective optimization algorithm via estimation of distribution. RM-MEDA employs a model-based method to generate new solutions, however, this method is easy to generate poor solutions when the population has no obvious regularity. To overcome this drawback, our proposed method add a new solution generation strategy, local learning, to the original RM-MEDA. Local learning produces solutions by sampling some solutions from the neighborhood of elitist solutions in the parent population. As it is easy to search some promising solutions in the neighborhood of an elitist solution, local learning can get some useful solutions which help the population attain a fast and accurate convergence. The experimental results on a set of test instances with variable linkages show that the implement of local learning can accelerate convergence speed and add a more accurate convergence to the Pareto optimal.

关键词： Multi-objective optimization problem estimation of distribution algorithm Local learning

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Metaheuristic-based time series clustering for anomaly detection in manufacturing industry

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APPLIED INTELLIGENCE 2023年第19期53卷 21723-21742页

作者： Suh, Woong Hyun Oh, Sanghoun Ahn, Chang Wook Gwangju Inst Sci & Technol GIST AI Grad Sch Gwangju 61005 South Korea Korea Natl Open Univ Dept Comp Sci Seoul 03087 South Korea

Nowadays time series clustering is of great importance in manufacturing industries. Meanwhile, it is considerably challenging to achieve explainable solution as well as significant performance due to computation complexity and variable diversity. To efficaciously handle the difficulty, this paper presents a novel metaheuristic-based time series clustering method which can improve the effectiveness and logicality of existing clustering approaches. The proposed method collects candidate cluster references from hierarchical and partitional clustering through shape-based distance measure as well as dynamic time warping (DTW) on manufacturing time series data. By applying metaheuristics highlighting estimation of distribution algorithms (EDA), such as extended compact genetic algorithm (ECGA), on the collected candidate clusters, advanced cluster centroid combinations with minimal distances can be achieved. ECGA employs the least complicated and the most closely related probabilistic model structure regarding population space during generation cycle. This feature strengthens the comprehension of clustering results in how such optimal solutions were achieved. The proposed method was tested on real-world time series data, open to the public, from manufacturing industry, and showed noticeable performances compared to well-established methods. Accordingly, this paper demonstrates that obtaining both comprehensible result as well as prominent performance is feasible by employing metaheuristic techniques to time series data clustering methods.

关键词： Metaheuristics Time series clustering Anomaly detection Manufacturing industry Evolutionary algorithm estimation of distribution algorithm Extended compact genetic algorithm Metaheuristic clustering Explainable artificial intelligence

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A full variate Gaussian model-based RM-MEDA without clustering process

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第10期9卷 1591-1608页

作者： Shi, Meifeng He, Zhongshi Chen, Ziyu Liu, Xin Chongqing Univ Coll Comp Sci Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing Peoples R China

A regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) is an excellent multi-objective estimation of distribution algorithm proposed in recent years. However, the performance of RM-MEDA is seriously affected by its clustering process. In order to avoid the influence of the clustering process, this paper presents a novel full variate Gaussian model-based (FGM-based) RM-MEDA without clustering process, named FRM-MEDA. In FRM-MEDA, the clustering process is removed from the original algorithm and the full variate Gaussian model (FGM) is introduced to keep the population diversity and make up the loss of the performance caused by removing the clustering process. Meanwhile, the introduction of FGM makes the FRM-MEDA faster and more stable when solving all the test instances. In addition, variable variance of FGM is presented to enhance the exploring ability of FRM-MEDA. The experiments demonstrate that the proposed algorithm significantly outperforms the RM-MEDA without clustering process and the RM-MEDA with K equal to AVE(K).

关键词： estimation of distribution algorithm Multi-objective optimization Number of clusters Full variate Gaussian model

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A hybrid EDA with ACS for solving permutation flow shop scheduling

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第9-12期60卷 1139-1147页

作者： Tzeng, Yeu-Ruey Chen, Chun-Lung Chen, Chuen-Lung Takming Univ Sci & Technol Dept Accounting Informat Taipei Taiwan Natl Chengchi Univ Dept MIS Taipei 11605 Taiwan

This paper proposes a hybrid estimation of distribution algorithm (EDA) with ant colony system (ACS) for the minimization of makespan in permutation flow shop scheduling problems. The core idea of EDA is that in each iteration, a probability model is estimated based on selected members in the iteration along with a sampling method applied to generate members from the probability model for the next iteration. The proposed algorithm, in each iteration, applies a new filter strategy and a local search method to update the local best solution and, based on the local best solution, generates pheromone trails (a probability model) using a new pheromone-generating rule and applies a solution construction method of ACS to generate members for the next iteration. In addition, a new jump strategy is developed to help the search escape if the search becomes trapped at a local optimum. Computational experiments on Taillard's benchmark data sets demonstrate that the proposed algorithm generated high-quality solutions by comparing with the existing population-based search algorithms, such as genetic algorithms, ant colony optimization, and particle swarm optimization.

关键词： Ant colony system estimation of distribution algorithm Permutation flow shop scheduling Makespan

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An effective hybrid EDA-based algorithm for solving multidimensional knapsack problem

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第5期39卷 5593-5599页

作者： Wang, Ling Wang, Sheng-yao Xu, Ye Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

In this paper, an effective hybrid algorithm based on estimation of distribution algorithm (EDA) is proposed to solve the multidimensional knapsack problem (MKP). With the framework of EDA, the probability model is built with the superior population and the new individuals are generated based on probability model. In addition, an updating mechanism of the probability model is proposed and a mechanism for initializing the probability model based on the specific knowledge of the MKP is also proposed to improve the convergence speed. Meanwhile, an adaptive local search is proposed to enhance the exploitation ability. Furthermore, the influences of parameters are investigated based on Taguchi method of design of experiment and the importance of repair operator is also studied via simulation testing and comparisons. Finally, numerical simulation is carried out based on the benchmark instances, and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multidimensional knapsack problem estimation of distribution algorithm Probability model Hybrid algorithm Design of experiment

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A two-stage approach for multicast-oriented virtual network function placement

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APPLIED SOFT COMPUTING 2021年 112卷 107798-107798页

作者： Wang, Xinhan Xing, Huanlai Zhan, Dawei Luo, Shouxi Dai, Penglin Iqbal, Muhammad Azhar Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China

Network function virtualization (NFV) is an emerging network paradigm that decouples softwarized network functions from proprietary hardware. Nowadays, resource allocation has become one of the hot topics in the NFV domain. In this paper, we formulate a service function chain (SFC) mapping problem in the context of multicast, which is also referred to as the multicast-oriented virtual network function placement (MVNFP) problem. The objective function considers end-to-end delay as well as compute resource consumption, with bandwidth requirements met. A two-stage approach is proposed to address this problem. In the first stage, Dijkstra's algorithm is adopted to construct a multicast tree. In the second stage, a novel estimation of distribution algorithm (nEDA) is developed to map a given SFC over the multicast tree. Simulation results show that the proposed two-stage approach outperforms a number of state-of-the-art evolutionary, approximation, and heuristic algorithms, in terms of the solution quality. (C) 2021 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithm Multicast Network function virtualization Virtual network function placement

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An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第8期52卷 2211-2231页

作者： Han, Yu-Yan Gong, Dun-wei Sun, Xiao-Yan Pan, Quan-Ke China Univ Min & Technol Sch Informat & Elect Engn Xuzhou Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China

Crossover and mutation operators in NSGA-II are random and aimless, and encounter difficulties in generating offspring with high quality. Aiming to overcoming these drawbacks, we proposed an improved NSGA-II algorithm (INSGA-II) and applied it to solve the lot-streaming flow shop scheduling problem with four criteria. We first presented four variants of NEH heuristic to generate the initial population, and then incorporated the estimation of distribution algorithm and a mutation operator based on insertion and swap into NSGA-II to replace traditional crossover and mutation operators. Last but not least, we performed a simple and efficient restarting strategy on the population when the diversity of the population is smaller than a given threshold. We conducted a serial of experiments, and the experimental results demonstrate that the proposed algorithm outperforms the comparative algorithms.

关键词： estimation of distribution algorithm multi-objective optimisation restarting strategy heuristic rule NSGA-II lot-streaming flow shop

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