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An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

ADVANCES IN ENGINEERING SOFTWARE 2016年第0期99卷 161-176页

作者： Lu, Chao Xiao, Shengqiang Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

This paper aims to provide a solution method for a real-world scheduling case from a welding process, which is one of the important processes in modern industry. The unique characteristic of the welding scheduling problem (WSP) is that multiple machines can process one operation at a time. Thus, WSP is a new scheduling problem. We first formulate a new multi-objective mixed integer programming model for this WSP based on a comprehensive investigation. This model involves some realistic constraints, controllable processing times (CPT), sequence dependent setup times (SDST) and job dependent transportation times (JDTT). Then we propose a multi-objective discrete grey wolf optimizer (MODGWO) considering not only production efficiency but also machine load on this real-world scheduling case. The solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. A reduction machine load strategy is used to adjust the number of machines aiming to minimize the machine load. To evaluate the effectiveness of the proposed MODGWO, we compare it with other well-known multi-objective evolutionary algorithms including NSGA-II and SPEA2 on a set of instances. Experimental results demonstrate that the proposed MODGWO is superior to the compared algorithms in terms of convergence, spread and coverage on most instances. Finally, MODGWO is successfully applied to this real-world WSP. This implies that the proposed model is feasible and the proposed algorithm can solve this real-world scheduling problem very well. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Welding scheduling multi-objective evolutionary algorithm Controllable processing times Sequence dependent setup times Transportation times Grey wolf optimizer

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multi-objective Software Effort Estimation: A Replication Study

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第8期48卷 3185-3205页

作者： Tawosi, Vali Sarro, Federica Petrozziello, Alessio Harman, Mark UCL Dept Comp Sci London WC1E 6BT England Facebook London WC1E 6BT England

Replication studies increase our confidence in previous results when the findings are similar each time, and help mature our knowledge by addressing both internal and external validity aspects. However, these studies are still rare in certain software engineering fields. In this paper, we replicate and extend a previous study, which denotes the current state-of-the-art for multi-objective software effort estimation, namely CoGEE. We investigate the original research questions with an independent implementation and the inclusion of a more robust baseline (LP4EE), carried out by the first author, who was not involved in the original study. Through this replication, we strengthen both the internal and external validity of the original study. We also answer two new research questions investigating the effectiveness of CoGEE by using four additional evolutionary algorithms (i.e., IBEA, MOCell, NSGA-III, SPEA2) and a well-known Java framework for evolutionary computation, namely JMetal (rather than the previously used R software), which allows us to strengthen the external validity of the original study. The results of our replication confirm that: (1) CoGEE outperforms both baseline and state-of-the-art benchmarks statistically significantly (p < 0.001);(2) CoGEE's multi-objective nature makes it able to reach such a good performance;(3) CoGEE's estimation errors lie within claimed industrial human-expert-based thresholds. Moreover, our new results show that the effectiveness of CoGEE is generally not limited to nor dependent on the choice of the multi-objective algorithm. Using CoGEE with either NSGA-II, NSGA-III, or MOCell produces human competitive results in less than a minute. The Java version of CoGEE has decreased the running time by over 99.8 percent with respect to its R counterpart. We have made publicly available the Java code of CoGEE to ease its adoption, as well as, the data used in this study in order to allow for future replication and extension of our wo

关键词： Software effort estimation multi-objective evolutionary algorithm confidence interval estimates uncertainty

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Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations

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STRUCTURAL AND multiDISCIPLINARY OPTIMIZATION 2010年第3期41卷 421-432页

作者： Goel, Tushar Stander, Nielen Lin, Yih-Yih Livermore Software Technol Corp Livermore CA USA Hewlett Packard Corp High Performance Comp Div Marlborough MA USA

This study pertains to practical use of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find an efficient allocation of the total simulation budget (population size and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting the number of simulations to 1,000. Parallelization is exploited using concurrent simulations for each GA generation on a HP quad-core cluster, and resulted in a significant time savings. Furthermore, the efficient distribution of computational effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers of generations were tried while limiting the total number of simulations to 1,000. The optimization performance was compared with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions could be found. It is shown that allowing a large number of generations is beneficial to get good trade-off solutions. For the vehicle design, significant improvements in the performance were observed. This example also suggests that, for problems with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations involving about 200 simulations.

关键词： multi-objective evolutionary algorithm Crashworthiness Population sensitivity Resource allocation NSGA-II Optimization

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multi-objective genetic algorithm for missing data imputation

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PATTERN RECOGNITION LETTERS 2015年第Part1期68卷 126-131页

作者： Lobato, Fabio Sales, Claudomiro Araujo, Igor Tadaiesky, Vincent Dias, Lilian Ramos, Leonardo Santana, Adamo Fed Univ Para R Augusto Correa Inst Technol Belem Para Brazil Fed Univ Western Para Engn & Geosci Inst Belem Para Brazil

A large number of techniques for data analyses have been developed in recent years, however most of them do not deal satisfactorily with a ubiquitous problem in the area: the missing data. In order to mitigate the bias imposed by this problem, several treatment methods have been proposed, highlighting the data imputation methods, which can be viewed as an optimization problem where the goal is to reduce the bias caused by the absence of information. Although most imputation methods are restricted to one type of variable whether categorical or continuous. To fill these gaps, this paper presents the multi-objective genetic algorithm for data imputation called MOGAImp, based on the NSGA-II, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and the modeling task. A set of tests for evaluating the performance of the algorithm were applied using 30 datasets with induced missing values;five classifiers divided into three classes: rule induction learning, lazy learning and approximate models;and were compared with three techniques presented in the literature. The results obtained confirm the MOGAImp outperforms some well-established missing data treatment methods. Furthermore, the proposed method proved to be flexible since it is possible to adapt it to different application domains. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Missing data Data imputation multi-objective evolutionary algorithm Genetic algorithm

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Micro multi-strategy multi-objective artificial bee colony algorithm for microgrid energy optimization

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 131卷 59-74页

作者： Peng, Hu Wang, Cong Han, Yupeng Xiao, Wenhui Zhou, Xinyu Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

multi-objective evolutionary algorithm (MOEA) has become a common and effective method to solve real-world multi-objective optimization problems. However, in some practical problems, such as the microgrid energy optimization problem (MEOP), the algorithm needs to run on the micro controller to control each distributed power supply in real time. Due limitation of hardware resources on the micro controller, the MOEAs are not suitable. The emerging micro population MOEAs are suitable for this scenario. But the micro population MOEA is vulnerable to lost diversity, resulting in its performance decline. Therefore, this paper proposes a new micro multi-strategy multi-objective ABC algorithm to solve MEOP, called mu MMABC. multi-strategy ABC optimizer is used to divide the population into multiple subgroups and produce offspring in parallel to balance the exploration and exploitation. In addition, an adaptive updating mechanism is proposed to renew the population adaptively. The mechanism can adaptively select more convergent and diverse solutions at different stages to balance the exploration and exploitation of the algorithm. Furthermore, in order to improve the performance of mu MMABC on problems with irregular Pareto fronts, the reference point reconstruction with intermediate strategy is also proposed. Some benchmark test suites are used to test the performance of mu MMABC. Finally, it is used to solve the MEOP. The experimental results show that the proposed algorithm is more competitive and effective than the traditional MOEAs and other micro population MOEAs in solving the MEOP. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Micro population multi-objective evolutionary algorithm Artificial bee colony Microgrid energy optimization Adaptive updating mechanism

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NON-DOMINATED SORTING METHODS FOR multi-objective OPTIMIZATION: REVIEW AND NUMERICAL COMPARISON

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2021年第2期17卷 1001-1023页

作者： Long, Qiang Wu, Xue Wu, Changzhi Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China

In multi-objective evolutionary algorithms (MOEAs), non-dominated sorting is one of the critical steps to locate efficient solutions. A large percentage of computational cost of MOEAs is on non-dominated sorting for it involves numerous comparisons. By now, there are more than ten different non-dominated sorting algorithms, but their numerical performance comparing with each other is not clear yet. It is necessary to investigate the advantage and disadvantage of these algorithms and consequently give suggestions to specific users and algorithm designers. Therefore, a comprehensively numerical study of non-dominated sorting algorithms is presented in this paper. Firstly, we design a population generator. This generator can generate populations with specific features, such as population size, number of Pareto fronts and number of points in each Pareto front. Then non-dominated sorting algorithms were tested using populations generated in certain structures, and results were compared with respect to number of comparisons and time consumption. Furthermore, In order to compare the performance of sorting algorithms in MOEAs, we embed them into a specific MOEA, dynamic sorting genetic algorithm (DSGA), and use these variations of DSGA to solve some multi-objective benchmarks. Results show that dominance degree sorting outperforms the other methods, fast non-dominance sorting performs the worst and the other sorting algorithms performs equally.

关键词： multi-objective optimization non-dominated sorting pareto front multi-objective evolutionary algorithm

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MRI appointment scheduling with uncertain examination time

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2019年第1期37卷 62-82页

作者： Qiu, Huaxin Wang, Dujuan Wang, Yanzhang Yin, Yunqiang Dalian Univ Technol Sch Management Sci & Engn Dalian 116023 Peoples R China KunmingUnivers Sci & Technol Data Sci Res Ctr Kunming 650093 Yunnan Peoples R China

This paper addresses the appointment scheduling problem for a single diagnostic facilitythe magnetic resonance imaging equipment, which provides several services to the appointed patients. The examinations have random service durations given by a joint discrete probability distribution. We consider two performance criteria: (1) the expected cost incurred from the equipment idle time and the examination overtime representing the operating costs of the hospital;and (2) the expected cost incurred from the patient waiting time reflecting the customer satisfaction. The overall goal is to identify the examination sequence and the scheduled start times for the appointed patients so as to minimize simultaneously the aforementioned criteria by determining all the Pareto-optimal schedules. The problem is first formulated as a two-stage stochastic integer programming model and it is shown to be NP-hard in the strong sense even for the case with only two scenarios. An improved multi-objective evolutionary algorithm is then proposed in the MOEA/D framework, where the uncertainty is simulated by constructing a number of different scenarios. To replace the time-consuming simulations during the process of evaluating the rescheduling cost, we integrate the algorithm with a support vector regression surrogate model which efficiently improves the robustness of the baseline schedule and the quality of the solution. Finally, using the real medical data, we assess the feasibility and effectiveness of the proposed model by comparing with the classical NSGA-II and the MOEA/D algorithm, and extract some appropriate management inspirations to medical staffs for decision-making references.

关键词： Appointment scheduling Duration uncertainty multi-objective evolutionary algorithm Support vector regression

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A data-driven optimisation method for a class of problems with redundant variables and indefinite objective functions

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INFORMATION SCIENCES 2024年 656卷

作者： Zhou, Jin Zhou, Kang Zhang, Gexiang Neri, Ferrante Shen, Wangyang Jin, Weiping Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430023 Peoples R China Chengdu Univ Informat Technol Sch Automat Chengdu 610225 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Grp Guildford GU27XH England Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210000 Peoples R China Wuhan Polytech Univ Sch Food Sci & Engn Wuhan 430023 Peoples R China

In the realm of practical problem-solving, multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) are becoming increasingly prevalent. MOPRVIF involve determining the optimal decision variables that optimise multiple objectives, leveraging the relational data of a set of variables and multiple objectives. For these problems, this paper focuses on the following two issues: one is the demand for a unified computational model to solve this problem;the other is how to improve the algorithm's deep intelligent search capability. In this regard, this paper designs a dual data-driven multi-objective optimisation method. The method used consisted of four parts: elimination of redundant variables (ERV), objective function construction (OFC), selection evolution operator (SEO), and multi-objective evolutionary algorithm (MOEA). MOEA was the main focus of the method. ERV is data preparation and variable selection according to multiple objectives. OFC involves constructing the relationship model between variables and objectives, and a high-accuracy model is important for guaranteeing reliable results. Furthermore, SEO can adjust the evolution operator during a deep search. This is an important guarantee for deep, intelligent search. MOEA combined OFC and SEO to form the final solution algorithm-Dual Data Driven multi-objective evolutionary algorithm (DDMOEA). DDMOEA was explored using two different disciplinary problems of drug compound optimisation and wild blueberry cultivation and benchmarks were selected. The first two problem domains are distinct. The first problem is more complex than the second;however, both encompass redundant variables and indefinite objective functions. Benchmarks are utilised independently to gauge the profound intelligent search capability. The experiments affirm that the dual data -driven optimization approach proposed in this paper is effective, practical, and scalable.

关键词： Data-driven optimisation method multi-objective evolutionary algorithm Adaptive optimisation

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An improved gradient-based NSGA-II algorithm by a new chaotic map model

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SOFT COMPUTING 2017年第23期21卷 7235-7249页

作者： Liu, Tan Gao, Xianwen Yuan, Qingyun Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ Natl Key Lab Integrated Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

Gradient-based non-dominated sorting genetic algorithm II (G-NSGA-II) is successful for solving multi-objective optimization problems. However, the effectiveness of gradient-based hybrid operator is influenced by the distribution of individuals in the population. In order to solve the problem, based on the framework of G-NSGA-II, we propose an improved gradient-based NSGA-II algorithm by introducing a new chaotic map model named IG-NSGA-II. In this algorithm, a new hybrid chaotic map model is first established to initialize population for keeping the diversity of the initial population. Then, the substitution operation of chaotic population candidate is introduced to maintain the diversity and uniformity of the Pareto optimal solution set. Finally, the proposed algorithm is tested on several standard test problems and compared with other algorithms. The experimental results indicate that the proposed algorithm leads to better performance results in terms of the convergence to Pareto front or the diversity of the obtained non-dominated solutions.

关键词： multi-objective evolutionary algorithm Gradient-based hybrid operator Hybrid chaotic map model Substitution operation of chaotic Non-dominated solutions

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multi-objective modeling and optimization for cleaner production processes

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JOURNAL OF CLEANER PRODUCTION 2006年第2期14卷 146-151页

作者： Jia, XP Zhang, TZ Wang, F Han, FY Tsinghua Univ Dept Environm Sci & Engn Beijing 100084 Peoples R China Univ Paderborn Inst Math D-33100 Paderborn Germany Qingdao Univ Sci & Technol Ctr Comp & Chem Engn Qingdao 266042 Peoples R China

The paper presents a methodology for multi-objective modeling and optimization with environmental impacts and economics aspects simultaneously in the context of cleaner production. A generalized multi-objective process model is proposed based on a holistic concept of macro-structure. A hybrid multi-objective evolutionary algorithm is proposed to solve this multi-objective optimization problem. The Elitist Non-dominated Sorting Genetic algorithm is used to obtain the Pareto-optimal set. The best compromised solution is determined by using the Technique for Order Preference by Similarity to the Ideal Solution. A simple reaction system is presented as a case study to illustrate the proposed methodology. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： process optimization environmental impacts multi-objective evolutionary algorithm cleaner production

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