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Dynamic quadratic decomposition-based evolutionary algorithm for multi-objective fuzzy flexible jobshop scheduling

SWARM AND evolutionary COMPUTATION 2025年 94卷

作者： Zhang, Xuwei Zhao, Ziyan Qin, Shujin Liu, Shixin Zhou, Mengchu Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shangqiu Normal Univ Sch Informat & Technol Shangqiu 476000 Peoples R China Northeastern Univ Qinhuangdao Hebei Key Lab Ind Software Adv Planning & Scheduli Qinhuangdao 066004 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs) have garnered widespread attention since they are able to handle the uncertainty of processing time in actual production. Nevertheless, making a good balance between the diversity and convergence of non-dominated solutions is a challenging issue that cannot be overlooked when MFFJSP is solved. To deal with these issues, this work proposes a Dynamic Quadratic Decomposition-based multi-objective evolutionary algorithm (DQD-MOEA) to solve MFFJSP by minimizing makespan and total machine workload. To solve a problem that the distribution and diversity of searched non-dominant solutions are poor due to the discrete decision space and objective space of MFFJSP, it proposes a dynamic quadratic decomposition method. Its core idea is to eliminate all the failed reference vectors because they have no intersection with areal Pareto front, and ensure that solutions evolve along effective reference vectors. This work also introduces a problem-specific local search method to accelerate the solution convergence for MFFJSP. It proposes a hybrid initialization method to improve the quality of initial solutions. Finally, a series of experiments are performed and the results demonstrate that DQD-MOEA is significantly better than state-of-the-art algorithms in terms of convergence and solution diversity when solving widely-tested benchmark cases.

关键词： multi-objective evolutionary algorithm Flexible jobshop scheduling Dynamic quadratic decomposition Fuzzy processing time

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Learning-guided bi-objective evolutionary optimization for green municipal waste collection vehicle routing

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JOURNAL OF CLEANER PRODUCTION 2025年 501卷

作者： Liao, Shubing Xu, Yixin Niu, Yunyun Cao, Zhiguang China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

Waste management has emerged as a critical issue in modern society, where vehicles are scheduled to visit multiple locations for waste collection and transport. This study focuses on a key problem in waste management: route optimization of waste collection vehicles, and formulate it as a bi-objective vehicle routing problem with stochastic demand (VRPSD), aiming to minimizing both total costs and carbon emissions. Although previous studies have significantly advanced our understanding of solving similar problems, the lack of real-world data and limited problem-solving capabilities still restrict the practical applicability of existing methods. To bridge this research gap, this study designed a regression model using nighttime light data to efficiently and accurately generate two real-case instances in Beijing. Furthermore, a multi-objective evolutionary algorithm integrates Efficient Non-dominated Sorting with Sequential Search and a one-dimensional convolutional neural network (MEAE1C) is proposed to solve the VRPSD problem. MEAE1C integrates a CNN evolver to leverage knowledge from current high-quality solutions to guide subsequent population evolution. Experimental results confirm the superior accuracy in estimates of waste generation, and extensive simulations on benchmark datasets and realcase scenarios consistently demonstrate the superiority of MEAE1C over existing methods. The above results highlight the practical feasibility of the proposed methods in addressing real-world municipal waste management challenges.

关键词： Almeida Municipal waste collection Vehicle routing problem multi-objective evolutionary algorithm 1DCNN Nighttime light NPP-VIIRS

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multi-objective evolutionary algorithms for MILP and MINLP in Process Synthesis

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Chinese Journal of Chemical Engineering 2001年第2期9卷 173-178页

作者：石磊姚平经 Dalian Univ Technol Lab Proc Syst Engn Dalian 116012 Peoples R China

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

关键词： multi-objective programming multi-objective evolutionary algorithm steady-state non-dominated sorting genetic algorithm process synthesis

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multi-objective carrier chaotic evolutionary algorithm for DNA sequences design

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Progress in Natural Science:Materials International 2007年第12期17卷 1515-1520页

作者： Xiao Jianhua, Xu Jin, Geng Xiutang and Pan Linqiang(Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China) Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.

关键词： DNA computing carrier chaotic search multi-objective evolutionary algorithm DNA sequences design.

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A new evolutionary optimization based on multi-objective firefly algorithm for mining numerical association rules

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SOFT COMPUTING 2024年第9-10期28卷 6879页

作者： Rokh, Babak Mirvaziri, Hamid Olyaee, Mohammadhossein Univ Zanjan Dept Comp Engn Zanjan *** Iran Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran Univ Gonabad Fac Engn Dept Comp Engn Gonabad Iran

Association rule mining (ARM) is a widely used technique in data mining for pattern discovery. However, association rule mining in numerical data poses a considerable challenge. In recent years, researchers have turned to optimization-based approaches as a potential solution. One particular area of interest in numerical association rules mining (NARM) is controlling the length of itemset intervals. In this paper, we propose a novel evolutionary algorithm based on the multi-objective firefly algorithm for efficiently mining numerical association rules (MOFNAR). MOFNAR utilizes Balance, square of cosine (SOC) and comprehensibility as objectives of evolutionary algorithm to assess rules and achieve a rule set that is both simple and accurate. We introduce the Balance measure to effectively control the intervals of numerical itemsets and eliminate misleading rules. Furthermore, we suggest a penalty approach, and the crowding-distance method is employed to maintain high diversity. Experimental results on five well-known datasets show the effectiveness of our method in discovering a simple rule set with high confidence that covers a significant percentage of the data.

关键词： Data mining Numerical association rule mining multi-objective evolutionary algorithm Firefly algorithm

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A novel non-dominated sorting algorithm for evolutionary multi-objective optimization

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年 23卷 31-43页

作者： Bao, Chunteng Xu, Lihong Goodman, Erik D. Cao, Leilei Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

evolutionary computation has shown great performance in solving many multi-objective optimization problems;in many such algorithms, non-dominated sorting plays an important role in determining the relative quality of solutions in a population. However, the implementation of non-dominated-sorting can be computationally expensive, especially for populations with a large number of solutions and many objectives. The main reason is that most existing non-dominated sorting algorithms need to compare one solution with almost all others to determine its front, and many of these comparisons are redundant or unnecessary. Another reason is that as the number of objectives increases, more and more candidate solutions become non-dominated solutions, and most existing time-saving approaches cannot work effectively. In this paper, we present a novel non-dominated sorting strategy, called Hierarchical Non Dominated Sorting (HNDS). HNDS first sorts all candidate solutions in ascending order by their first objective. Then it compares the first solution with all others one by one to make a rapid distinction between different quality solutions, thereby avoiding many unnecessary comparisons. Experiments on populations with different numbers of solutions, different numbers of objectives and different problems have been done. The results show that HNDS has better computational efficiency than fast non-dominated sort, Arena's principle and deductive sort. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Non-dominated sort Pareto front Computational complexity

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A dual-sampling based evolutionary algorithm for large-scale multi-objective optimization

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Zhang, Weiwei Wang, Sanxing Li, Guoqing Zhang, Weizheng Wang, Xiao Zhengzhou Univ Light Ind Sch Comp Sci & Technol Zhengzhou 450000 Peoples R China Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China

The vast search space in large-scale multi-objective optimization represents a significant challenge for evolutionary algorithms to converge towards the Pareto Front. As an effective search strategy, direction-guided sampling technique could improve the search efficiency by exploring along the approximated directions to approach the Pareto set. However, the approximated directions may fail to interact with the true Pareto set and result in inefficient search. To address this issue, a dual-sampling method is proposed in this paper. In addition to the samples along the directions approximated by direction-guided sampling, fuzzy Gaussian sampling is applied to adjust the search direction and generate more accurate and evenly distributed solutions. Moreover, a convergence-and-diversity-based mating selection is introduced to balance the exploration and exploitation. The experiments on 72 test benchmarks with bi- and tri-objectives and 500-5000 decision variables show the superiority of the proposed algorithm compare with the state-of-the-art algorithms.

关键词： multi-objective evolutionary algorithm Large-scale multi-objective optimization Direction-guided sampling Reproduction

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NSGA-II/EDA Hybrid evolutionary algorithm for Solving multi-objective Economic/Emission Dispatch Problem

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第10期46卷 1158-1170页

作者： Alawode, Kehinde Olukunmi Adegboyega, Gabriel Adebayo Muhideen, Jubril Abimbola Osun State Univ Dept Elect & Elect Engn Osogbo Nigeria Obafemi Awolowo Univ Dept Elect & Elect Engn Ife Nigeria

In this study, a hybrid algorithm which combines the NSGA-II with a modified form of the marginal histogram model Estimation of Distribution algorithm (EDA), herein called the NSGA-II/EDA is proposed for solving the multi-objective economic/emission power dispatch problem. The goal is to improve the convergence while preserving the diversity properties of the obtained solution set. The effect of variable interaction on the marginal histogram EDA model is reduced by performing multi-scale Principal Component Analysis on the population of solutions. Also, the concepts of non-domination and elitism have been introduced into the marginal histogram model in order for it to handle multiple objectives. Several optimization runs were carried out on the standard multi-objective test problems, including the IEEE 30- and the 118-bus test systems. Standard metrics are used to compare the performance of the developed hybrid approach with that of other multi-objective evolutionary algorithms. The effectiveness of the proposed approach in improved convergence, with good diversity is demonstrated.

关键词： multi-objective evolutionary algorithm hybrid evolutionary algorithm combined economic-emission dispatch problem

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Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm☆

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Hao, Linyuan Zou, Zhiyuan Liang, Xu Beijing Informat Sci & Technol Univ Comp Sci Beijing Peoples R China

For the issue of energy consumption in the multi-objective flexible job shop scheduling problem (MOFJSP), balancing machine load is significant for enhancing environmental sustainability and cost efficiency in intelligent manufacturing. Most studies overlook the critical role of machine load in energy consumption. Therefore, this paper establishes a multi-objective energy-saving flexible job shop scheduling model with the objectives of minimizing the maximum and total machine load, makespan, then proposes a hybrid search genetic algorithm (HSGA) to solve this problem. Firstly, for enhancing population diversity, this paper proposes a cluster-based initial solution selection strategy, preventing the issue of limited search range caused by high similarity among initial solutions. Secondly, to broaden the search scope for multi-objective optimal solutions, this paper proposes a population selection strategy that considers individual neighborhood density and designed multiple neighborhood operators for variable neighborhood search (VNS). Finally, this paper proposes an adaptive strategy to dynamically adjust crossover and mutation probabilities for genetic operators, achieving a balance between global and local search. Experimental results show that the HSGA exhibits superior performance.

关键词： multi-objective optimization Flexible job shop scheduling problem multi-objective evolutionary algorithm

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A model and algorithm for reactive multi-objective multi-skilled project scheduling under resource disruptions

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Su, Yixuan Xu, Zhe Liu, Dongning Beihang Univ Sch Econ & Management 37 Xueyuan Rd Beijing 100191 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

The reactive multi-objective multi-skilled project scheduling under resource disruptions is more complex than the general scheduling problem. This complexity stems from the incorporation of overtime and proactive preemption as response strategies, as well as the consideration of the impact of a resource's skill level on setup time. We propose a reactive multi-objective scheduling model to address this problem. The objective is to generate a new schedule that minimizes deviations in activity start times and resource allocations from the baseline schedule, while also minimizing resource usage costs. To enhance the efficiency of commercial solvers in solving this model, some of the formulas are linearized. Furthermore, we propose a hybrid multi-objective evolutionary algorithm (HMOEA). To boost the performance of the HMOEA, several improvement strategies are introduced, including an adaptive procedure for fitness assignment and density estimation, a hybrid evolutionary strategy, and a local search strategy. Numerical experiments demonstrate the effectiveness of both the linearization measures and the improvement strategies. Five performance metrics and convergence comparisons are employed to assess the solution quality of the proposed algorithm in terms of convergence, diversity, and distribution. Computational results demonstrate that HMOEA leads to significant improvements in all metrics compared to five state-of-the-art algorithms.

关键词： multi-skilled Reactive project scheduling Resource disruption multi-objective evolutionary algorithm

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