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Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Kinoshita, Takato Masuyama, Naoki Nojima, Yusuke Osaka Prefecture Univ Guraduate Sch Engn Sakai Osaka Japan Osaka Metropolitan Univ Grad Sch Informat Sakai Osaka Japan

ISBN: (纸本)9781665499248

Many tasks in the real world are multi-objective optimization problems (MOPs). Population-based approaches are promising for solving MOPs. In particular, multi-objective evolutionary algorithms (MOEAs) are popular and have been actively studied over the last two decades. However, since it is not easy to directly display and compare multi-dimensional solution sets, it is difficult to analyze the search process of MOEAs using direct visualization techniques such as scatter plots. This paper proposes an analytical method to compare multiple search processes in terms of convergence and diversity by extending the authors' previous work, i.e., Convergence-Diversity Diagram. Through computational experiments, the proposed method reveals characteristics and similarities in three representative MOEAs and six test problems. In addition, this paper provides discussions on algorithm design, biases in the DTLZ test suite, and the improvement of visualization based on experimental results.

关键词： multi-objective optimization multi-objective evolutionary algorithm search process analysis convergence diversity

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An interactive method for multi-satellite imaging mission planning in complex environments 36

An interactive method for multi-satellite imaging mission pl...

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36th Chinese Control and Decision Conference (CCDC)

作者： Yang, Xueying Hu, Min Huang, Gang Space Engn Univ Dept Aerosp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Existing multi-satellite imaging mission planning methods mostly apply to scenarios with clear problem boundaries, explicit decision rules, and limited problem sizes. It is difficult to deal with the problem that the real environment is more complex and the action effect is more uncertain. This paper proposes a Human-machine interactive mechanism based on knowledge transfer strategy multi-objective evolutionary algorithm (HMIM-KTSMOEA) for multi-satellite Imaging Mission Planning in Complex Environments (MSIMPCE). Firstly, a Human-machine interactive mechanism is proposed to deal with the dynamic changes of the environment effectively. Secondly, a multi-objective evolutionary algorithm based on a knowledge transfer strategy is proposed to improve the efficiency of MSIMPCE, which has complex constraints and large solving space. Simulation results show that HMIM-KTSMOEA has obvious advantages in solving the MSIMPCE problem in terms of task execution efficiency and response time.

关键词： multi-satellite imaging mission planning active method multi-objective evolutionary algorithm

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multi-objective evolutionary Instance Selection for multi-label Classification 19th

Multi-objective Evolutionary Instance Selection for Multi-la...

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Liu, Dingming Shang, Haopu Hong, Wenjing Qian, Chao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031208614;9783031208621

multi-label classification is an important topic in machine learning, where each instance can be classified into more than one category, i.e., have a subset of labels instead of only one. Among existing methods, ML-kNN [25], the direct extension of k-nearest neighbors algorithm to the multi-label scenario, has received much attention due to its conciseness, great interpretability, and good performance. However, ML-kNN usually suffers from a terrible storage cost since all training instances need to be saved in the memory. To address this issue, a natural way is instance selection, intending to save the important instances while deleting the redundant ones. However, previous instance selection methods mainly focus on the single-label scenario, which may have a poor performance when adapted to the multi-label scenario. Recently, few works begin to consider the multi-label scenario, but their performance is limited due to the inapposite modeling. In this paper, we propose to formulate the instance selection problem for ML-kNN as a natural bi-objective optimization problem that considers the accuracy and the number of retained instances simultaneously, and adapt NSGA-II to solve it. Experiments on six real-world data sets show that our proposed method can achieve both not worse prediction accuracy and significantly better compression ratio, compared with state-of-the-art methods.

关键词： multi-label classification ML-kNN Instance selection multi-objective optimization multi-objective evolutionary algorithm

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Image Deformation Estimation via multiobjective Optimization

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IEEE ACCESS 2022年 10卷 53307-53323页

作者： Nakane, Takumi Xie, Haoran Zhang, Chao Univ Fukui Dept Engn Fukui 9108507 Japan Japan Adv Inst Sci & Technol Nomi 9231211 Japan

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.

关键词： Strain Estimation Optimization Deformable models Computational modeling evolutionary computation Genetic algorithms Genetic algorithm image deformation estimation image registration multi-objective evolutionary algorithm

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multi-objective evolutionary architectural pruning of deep convolutional neural networks with weights inheritance

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INFORMATION SCIENCES 2024年 685卷

作者： Chung, K. T. Lee, C. K. M. Tsang, Y. P. Wu, C. H. Asadipour, Ali Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Sci Pk Hong Kong Peoples R China Hang Seng Univ Hong Kong Dept Supply Chain & Informat Management Hong Kong Peoples R China Royal Coll Art Comp Sci Res Ctr London England

Despite the ongoing success of artificial intelligence applications, the deployment of deep learning models on end devices remains challenging due to the limited onboard computational resources. A way to tackle this challenge is model compression through network pruning, which removes unnecessary parameters to reduce model size without significantly affecting performance. However, existing iterative methods require designated pruning rates and obtain a single pruned model, which lacks the flexibility to adapt to devices with heterogeneous computational capabilities. This paper considers network pruning in Deep Convolutional Neural Networks (DCNNs) and proposes a novel algorithm for structured filter pruning in DCNNs using a multi- objective evolutionary approach with a novel weights inheritance scheme and representation scheme to reduce the time cost of the optimization process. The proposed method provides solutions with multiple levels of tradeoff between performance and efficiency for various hardware specifications on edge devices. Experimental results demonstrate the effectiveness of the proposed framework in optimizing popular DCNN models in terms of model complexity and accuracy. Notably, the framework successfully made significant reductions in floating-point operations ranging from 40% to 90% of VGG-16/19 and ResNet-56/110 with negligible loss in accuracy on the CIFAR-10/100 dataset.

关键词： Network pruning multi-objective evolutionary algorithm Deep convolutional neural networks Deep learning

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A Hybrid evolutionary algorithm with Heuristic Mutation for multi-objective Bi-clustering

A Hybrid Evolutionary Algorithm with Heuristic Mutation for ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Bechikh, Slim Elarbi, Maha Hung, Chih-Cheng Hamdi, Sabrine Ben Said, Lamjed Kennesaw State Univ Kennesaw GA 30144 USA Anyang Normal Univ Anyang Peoples R China Univ Tunis SMART Lab ISG Campus Tunis Tunisia

ISBN: (纸本)9781728121536

Bi-clustering is one of the main tasks in data mining with several application domains. It consists in partitioning a data set based on both rows and columns simultaneously. One of the main difficulties in bi-clustering is the issue of finding the number of bi-clusters, which is usually a user-specified parameter. Recently, in 2017, a new multi-objective evolutionary clustering algorithm, called MOCK-Il, has shown its effectiveness in data clustering while automatically determining the number of clusters. Motivated by the promising results of MOCK-Il, we propose in this paper a hybrid extension of this algorithm for the case of bi-clustering. Our new algorithm, called MOBICK, uses an efficient solution encoding, an effective crossover operator, and a heuristic mutation strategy. Similarly to MOCK-II, MOBICK is able to find automatically the number of bi-clusters. The outperformance of our algorithm is shown on a set of real gene expression data sets against several existing state-of-the-art works. Moreover, to be able to compare MOBICK to MOCK-I and MOCK-Il, we have designed two basic extensions of MOCK-I and MOCK-Il for the case of bi-clustering that we named B-MOCK-I and B-MOCK-II. Again, the experimental results confirm the merits of our proposal.

关键词： Bi-clustering multi-objective evolutionary algorithm hybridization heuristic mutation reduced-length genetic encoding

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Bi-objective no-wait multiproduct multistage product scheduling problem with flexible due dates based on MOIDE- MA

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COMPUTERS & OPERATIONS RESEARCH 2022年 137卷 105543-105543页

作者： Yan, Xueli Gu, Xingsheng East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China

This paper deals with the no-wait multiproduct multistage product scheduling problem (NWMMSP) with minimum makespan and maximum mean customer's satisfaction level criteria. The mean customer's satisfaction level criterion related to flexible due date is introduced to approach an imprecise or flexible nature of the data in a practical manufacturing environment. A novel discrete multi-objective evolutionary algorithm combined with an improved differential evolution algorithm and memetic algorithm (MOIDE-MA) is proposed to solve the multiobjective NWMMSP. The improved discrete differential evolution algorithm (IDE) mainly focuses on the edge area of the Pareto front (PF) space and the single objective guide strategy to construct better reference points. Meanwhile, an information-sharing strategy is introduced in IDE to improve the information diversity and enhance the search capabilities for the whole PF. A novel memetic algorithm (MA) based on genetic algorithm (GA) and local search related to the researched scheduling problem is developed to improve the distribution uniformity and accuracy of the non-dominated solutions obtained by IDE further. IDE gains some useful information extracted from the external archive of MA to speed up its search. Computational and simulation results show that the improvement strategy proposed in the algorithm can effectively improve the performance of MOIDE-MA. Furthermore, the proposed algorithm is compared with three other multi-objective scheduling optimization approaches based on different scale multi-objective NWMMSP instances. The experimental results show the effectiveness of the proposed MOIDE-MA.

关键词： No-wait multiproduct multistage scheduling problem Flexible due dates Makespan multi-objective evolutionary algorithm Differential evolution algorithm Memetic algorithm

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A novel solver formulti-objective optimization: dynamic non-dominated sorting genetic algorithm (DNSGA)

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SOFT COMPUTING 2022年第2期26卷 725-747页

作者： Long, Qiang Li, Guoquan Jiang, Lin Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 400047 Peoples R China Curtin Univ Sch Engn Comp & Math Bentley WA 6102 Australia

Non-dominated sorting is a critical component of all multi-objective evolutionary algorithms (MOEAs). A large percentage of computational cost of MOEAs is spent on non-dominated sorting. So, the complexity of non-dominated sorting method in a large extent decides the efficiency of the MOEA. In this paper, we present a novel non-dominated sorting method called the dynamic non-dominated sorting (DNS). It is based on the sorting of real number sequence instead of dominance comparisons. The computational complexity of DNS is O(mN log N) (m is the number of objectives, N is the population size), which equals to the best record so far. In the numerical experiments, we verify the outperformance of DNS comparing with other non-dominated sorting methods. Based on DNS, we introduce a novel multi-objective genetic algorithm called the dynamic non-dominated sorting genetic algorithm (DNSGA). Numerical experiments on DNSGA are also given. The results show that DNSGA outperforms some other MOEAs on both general-scale and large-scale multi-objective problems.

关键词： Dynamic non-dominated sorting genetic algorithm multi-objective optimization multi-objective evolutionary algorithm Test problems

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An adaptive multiobjective evolutionary algorithm based on grid subspaces

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MEMETIC COMPUTING 2021年第2期13卷 249-269页

作者： Li, Linlin Wang, Xianpeng Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang 110819 Peoples R China Liaoning Key Lab Mfg Syst & Logist Liaoning Engn Lab Operat Analyt & Optimizat Smart Shenyang 110819 Peoples R China

The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds of multiobjective problems have attracted considerable attention in recent years. In this paper, an adaptive multi-objective evolutionary algorithm is proposed by incorporating the concepts of the grid system (denoted as AGMOEA). Based on grid, the objective space is divided into subspaces. Based on the quality and dominance relationship between subspaces, the evolutionary opportunities are dynamically allocated to different subspaces with an adaptive selection strategy. To improve the evolutionary efficiency, the evolutionary scheme and an external archive mechanism considering representative individuals are proposed. The experimental results on 21 benchmark problems demonstrate that the proposed algorithm is competitive or superior to the rival algorithms.

关键词： Grid External archive multi-objective evolutionary algorithm Dominance relationship

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multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages

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RENEWABLE ENERGY 2022年第0期184卷 776-790页

作者： He, Yi Guo, Su Zhou, Jianxu Ye, Jilei Huang, Jing Zheng, Kun Du, Xinru Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211000 Peoples R China Nanjing Technol Univ Sch Energy Sci & Engn Nanjing 211816 Peoples R China

In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models;(2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic algorithm-II (NSGA-II);(3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Renewable energy Hybrid energy storage Operation threshold optimization Planning-operation co-optimization multi-objective evolutionary algorithm Data-driven model

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