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An active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第11期20卷 19839-19857页

作者： Guo, Xiaofang Wang, Yuping Zhang, Haonan Xian Technol Univ Sch Sci Xian 710000 Peoples R China

The inverse model based multi-objective evolutionary algorithm (IM-MOEA) generates offspring by establishing probabilistic models and sampling by the model, which is a new computing schema to replace crossover in MOEAs. In this paper, an active learning Gaussian modeling based multi-objective evolutionary algorithm using population guided weight vector evolution strategy (ALGM-MOEA) is proposed. To properly cope with multi-objective problems with different shapes of Pareto front (PF), a novel population guided weight vector evolution strategy is proposed to dynamically adjust search directions according to the distribution of generated PF. Moreover, in order to enhance the search efficiency and prediction accuracy, an active learning based training sample selection method is designed to build Gaussian process based inverse models, which chooses individuals with the maximum amount of information to effectively enhance the prediction accuracy of the inverse model. The experimental results demonstrate the competitiveness of the proposed ALGM-MOEA on benchmark problems with various shapes of Pareto front.

关键词： active learning multi-objective evolutionary algorithm weight vector adjustment Gaussian regression model Pareto front

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A multi-objective evolutionary algorithm With Hierarchical Clustering-Based Selection

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IEEE ACCESS 2023年 11卷 2557-2569页

作者： Zhou, Shenghao Chen, Ze Li, Qi Gu, Mengjun Bao, Zhoucheng He, Wenda Sheng, Weiguo Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China Hangzhou Normal Univ Engn Res Ctr Mobile Hlth Management Syst Hangzhou 311121 Peoples R China China Telecom Corp Ltd Zhejiang Branch Hangzhou 310022 Peoples R China

This paper proposes an evolutionary algorithm with hierarchical clustering based selection for multi-objective optimization. In the proposed algorithm, a hierarchical clustering is employed to design the environmental and mating selections, named local coverage selection and local area selection, respectively, for multi-objective evolutionary algorithm. The local coverage selection strategy aims to preserve well-distributed individuals with good convergence. While, the local area selection strategy is devised to deliver a balanced evolutionary search. This is achieved by encouraging individuals for exploration or exploitation according to the I-epsilon+ indicator. In both strategies, a hierarchical clustering method is employed to discover the population structure. Based on the clustering results, in local coverage selection, the individuals of different clusters will be retained according to their coverage areas and crowding distances, such that distributing as evenly as possible in the Pareto front. In local area selection, the individual(s) with the best value of I-epsilon+ indicator in each cluster will be selected to perform mating, with the purpose of achieving a balanced exploration and exploitation. The proposed algorithm has been evaluated on 26 bench-mark problems and compared with related methods. The results clearly show the significance of the proposed method.

关键词： multi-objective evolutionary algorithm multi-objective optimization problem hierarchical clustering

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Dual population multi-objective evolutionary algorithm for dynamic co-transformations

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evolutionary INTELLIGENCE 2024年第5-6期17卷 3269-3289页

作者： Yang, Yongkuan Yang, Yanxiang Liao, Binrong Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China

Solving constrained multi-objective optimization problems is a challenging task and existing algorithms have been struggling to balance constrained convergence and population diversity. To address this problem, this paper proposes a dual-population cooperative algorithm (DPDCA), which maintains two populations and an archive, and can change the search strategy depending on the evolutionary stage. At the early stage of evolution, the two populations are responsible for different tasks, promote rapid convergence of the populations by exchanging solutions, and save promising solutions in the archive. In the later stages of evolution, different local search strategies are emphasized according to the diversity of the main populations and their convergence to obtain a feasible solution set with better convergence. In order to prevent the algorithm from falling into local optimality at a later stage, we introduce the archive to enhance the diversity of the populations, and finally the main populations are explored in depth to obtain the set of solutions located in the PF. To test the superiority of the algorithm, we tested it against five state-of-the-art algorithms on 28 benchmark test problems and 3 real-world problems, proving that the altered algorithm has good competitiveness in dealing with constrained multi-objective optimization problems.

关键词： Dual-population multi-objective evolutionary algorithm Archives Dynamic synergy strategy

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A phenotype-based multi-objective evolutionary algorithm for maximizing lifetime in wireless sensor networks with bounded hop

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SOFT COMPUTING 2023年第15-16期28卷 8681页

作者： Ngoc, Bui Hong Tam, Nguyen Thi Binh, Huynh Thi Thanh Vinh, Le Trong Hanoi Univ Sci & Technol Hanoi Vietnam Vietnam Natl Univ Univ Sci Hanoi Vietnam

Relay node placement with a hop count bound is a crucial problem in enhancing connectivity, lifetime, and reliability in multi-hop wireless sensor networks. However, existing approaches focus solely on minimizing the number of used relay nodes without considering the energy consumption among nodes. This work investigates a relay node placement problem in multi-hop wireless sensor networks with two objectives: minimize the number of used relay nodes, and minimize the maximum node energy consumption to prolong the network's lifetime while still ensuring the network's connectivity. In particular, we consider a hop count bound as a delay constraint to elevate the network's reliability. We propose a multi-objective evolutionary algorithm called GPrim to solve our problem. The algorithm is a combination of edge-set encoding and NSGA-II framework. Leveraging problem-specific properties, we introduce objective-oriented heuristics incorporated into initialization, crossover, and mutation operators to improve the algorithm's convergence. Simulation results on 3D datasets show that the proposed algorithm performs significantly better than existing algorithms on all measured metrics.

关键词： multi-hop wireless sensor networks Network lifetime multi-objective evolutionary algorithm

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An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第10期237卷 1553-1569页

作者： Sun, Xiang Guo, Shunsheng Guo, Jun Du, Baigang Tang, Hongtao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China Hubei Digital Mfg Key Lab Wuhan Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn 122 Luoshi Rd Wuhan 430070 Peoples R China

The product's service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.

关键词： Selective disassembly sequencing planning asynchronous parallel disassembly multi-objective evolutionary algorithm pareto frontier remanufacturing

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A Novel multi-objective evolutionary algorithm Hybrid Simulated Annealing Concept for Recommendation Systems 1

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23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Du, Yu Bao, Haijia Li, Ya Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

Nowadays, recommendation systems have been widely used in information systems and internet applications. In order to solve the problem that most traditional recommendation algorithms mainly focus on accuracy and neglect other requirements such as diversity and novelty, multi-objective evolutionary algorithm has been introduced into recommendation system. But there is still much room for improvement in their performance. In this paper, we design a novel multi-objective evolutionary algorithm for recommendation system with accuracy, diversity, and novelty as its objective functions. In the proposed model, an efficient uniform distribution initialization and a mutation operator that integrates the concept in simulated annealing algorithm has been designed to improve the probability of producing more high-quality offspring. And an adaptive hybrid selection strategy is designed to select more valuable or promising individuals from offspring. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of accuracy, diversity, novelty, and hypervolume compared to some state-of-the-art recommendation algorithms.

关键词： multi-objective evolutionary algorithm Recommendation system Genetic operator

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Modifying Belief Propagation MIMO Detector with multi-objective evolutionary algorithm 5

Modifying Belief Propagation MIMO Detector with Multi-Object...

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5th Information Communication Technologies Conference (ICTC)

作者： Wang, Zihe Tan, Xiaosi Zhou, Wenyue Huang, Yongming Zhang, Chuan Southeast Univ Lab Efficient Architectures Digital Commun & Sign Natl Mobile Commun Res Lab Nanjing Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9798350373530;9798350373523

This paper proposes modified-BP (mBP), a novel approach that leverages a multi-objective evolutionary algorithm (MOEA) to enhance belief propagation (BP) for massive multi-pleinput multiple-output (MIMO) detection. mBP optimizes the BP algorithm by determining the optimal search space size and number of iterations for improved computational efficiency. Numerical results demonstrate the significant reduction in computational complexity of mBP compared to the state-of-the-art belief-selective propagation (BsP) algorithm. Additionally, mBP shows negligible performance degradation in most scenarios, with some configurations even exhibiting slight performance enhancement, which highlights mBP's potential as an efficient and effective detector for MIMO systems.

关键词： Massive MIMO belief propagation multi-objective evolutionary algorithm NSGA-II

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multi-time constrained dual-resource flexible job shop scheduling based on multi-objective evolutionary algorithm 4

Multi-time constrained dual-resource flexible job shop sched...

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4th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

作者： Yang, Luda Lv, Zhuoxuan Du, Baigang Guo, Jun Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9798350375084;9798350375077

This paper presents a new generalization of the uniform dual-resource constrained flexible job shop scheduling problem (DRCFJSP) for automated flexible manufacturing system, considering two multi-time constraints of setup time and transportation time. Design a mixed integer nonlinear programming model with multiple optimization objectives of minimizing maximum completion time, minimizing transportation time and minimizing setup time. And a hybrid multi-objective evolutionary algorithm (HMOEA) is proposed to solve the problem. Through simulation experiments on different data sets and algorithm comparison, the effectiveness and feasibility of the proposed algorithm are verified. Finally, we designed thirteen processing cases under different conditions and compared them with four different comparison algorithms, and concluded that the HMOEA algorithm is superior in solving the DRCFJSP problem, which is of great significance to the use of algorithms for optimal design in actual production.

关键词： Flexible manufacturing systems Dual-resource constrained flexible job shop scheduling Setup and transportation time multi-objective evolutionary algorithm

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On Performance of a Simple multi-objective evolutionary algorithm on the Constrained Minimum Spanning Tree Problem

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-11页

作者： Lai, Xinsheng Shaoxing Univ Dept Comp Sci & Engn Beihai St Shaoxing 312000 Zhejiang Peoples R China

The constrained optimization problems can be transformed into multi-objective optimization problems, and thus can be optimized by multi-objective evolutionary algorithms. This method has been successfully used to solve the constrained optimization problems. However, little theoretical work has been done on the performance of multi-objective evolutionary algorithms for the constrained optimization problems. In this paper, we theoretically analyze the performance of a multi-objective evolutionary algorithms on the constrained minimum spanning tree problem. First, we theoretically prove that the multi-objective evolutionary algorithm is capable of finding a (2,1)-approximation solution for the constrained minimum spanning tree problem in a pseudopolynomial runtime. Then, this simple multi-objective evolutionary algorithm is shown to be efficient on a constructed instance of the problem.

关键词： Approximation algorithm multi-objective evolutionary algorithm Constrained optimization Spanning tree problem Combinatorial problem

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Probability Learning Based multi-objective evolutionary algorithm for Distributed No-Wait Flow-Shop and Vehicle Transportation Integrated Optimization Problem 19th

Probability Learning Based Multi-objective Evolutionary Algo...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Ding, Ziqi Li, Zuocheng Qian, Bin Hu, Rong Zhang, Changsheng Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

This paper proposes a probability learning based multi-objective evolutionary algorithm (PLMOEA) to solve distributed no-wait flow-shop and vehicle transportation integrated optimization problem (DNFVTIOP). The objectives to be minimized are the total completion time for each factory (TT) and the total transportation cost (TC). Firstly, we propose for the first time the mathematical description of DNFVTIOP that is quite different from previous works. Secondly, a probability learning approach based on two probability matrices are introduced to improve the global search ability of PLMOEA. To be specific, a positioning probability matrix (PM) is used to accumulate the probability of each job occurring at each position in high-quality solutions. And a conjoint structure probability matrix (CM) is used to learn the dependency of two joint jobs in high-quality solutions. Next, the new population is generated through a dedicated cooperation strategy based on the nondominated sorting method. Finally, computational results show that PLMOEA is an effective solution method for DNFVTIOP.

关键词： Probability learning multi-objective evolutionary algorithm distributed no-wait flow-shop vehicle transportation integrated optimization

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