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2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Liu, Ru Lei, Hongzhen International Business School Shaanxi Normal University Xi'an710119 China

ISBN: (纸本)9798400716478

When making decisions about collaborative innovation, algorithms can provide› significant assistance to decision-makers. The market for new energy vehicles is vast and highly competitive, making it crucial to elevate innovation levels and master core technologies. As market uncertainty rapidly increases, independently conducting technical research and development becomes increasingly challenging for enterprises. Therefore, collaborative innovation has become an essential innovation model for enterprises. This paper, in conjunction with the actual industrial development situation, conducts experimental research on the behavior of Artificial Intelligence driven algorithms, specifically employing Q-learning algorithm is configured with a step size (δt) set to 0.1, an exploration-exploitation parameter (τ) set to 10, and a discount factor (δ) set to 0.95. The study focuses on collaborative innovation in new energy vehicle enterprises, aiming to analyze possible outcomes and reveal the dynamics of the collaborative innovation process influenced by Artificial Intelligence. The results indicate that initial strategies play a crucial role in collaborative innovation, and in some cases, even if enterprises do not collaborate frequently initially, they can learn strategies for permanent collaboration. Additionally, the algorithm can learn collusion through a reward-punishment mechanism but needs room for tolerance to allow the algorithm to re-coordinate to maintain the results of permanent collaboration. © 2023 ACM.

关键词： evolutionary algorithms

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Predicting impact of land cover change on flood peak using hybrid machine learning models

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6723-6736页

作者： Sedighkia, Mahdi Datta, Bithin Australian Natl Univ ICEDS Canberra ACT Australia James Cook Univ Coll Sci & Engn Townsville Qld Australia

The present study evaluates the performance of hybrid machine learning models to predict flood peak due to land cover changes. Performance of feed forward neural network (FNN) and adaptive neuro-fuzzy inference system (ANFIS) was compared and analyzed to select the best model in which different conventional training algorithms and evolutionary algorithms were applied in the training process. The inputs consist of stream flow in previous time step, rainfall and area of each land use class, and output of the model is stream flow in the current time step. The models were trained and tested based on the available data in a river basin located in the Australian tropical region. Based on the results in the case study, invasive weed optimization is the best method to train the machine learning system for simulating flood peak. In contrast, some optimization algorithms such as harmony search algorithm are very weak to train the machine learning model. Furthermore, results corroborated that the performance of FNN and NFIS is the same in terms of generality. The FNN model is more reliable to predict the flood peak in the case study. Moreover, ANFIS-based model is more complex than FNN. However, ANFIS is advantageous in terms of interpretability. The main weakness of ANFIS-based model is underestimation of flood peak in the major and minor floods. Two scenarios of changing land cover were tested which demonstrated reducing natural cover might increase the flood peak more than twice.

关键词： Land cover change Flood peak Neural networks evolutionary algorithms Fuzzy inference system

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Temporal distribution-based prediction strategy for dynamic multi-objective optimization assisted by GRU neural network

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INFORMATION SCIENCES 2023年第1期649卷

作者： Hou, Xing Ge, Fangzhen Chen, Debao Shen, Longfeng Zou, Feng Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei 235000 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei 235000 Anhui Peoples R China

To solve dynamic multi-objective optimization problems, evolutionary algorithms must be capable of quickly and accurately tracking the changing Pareto front such that they can respond in a timely and effective manner when detecting environmental changes. To address this challenge, we propose a dynamic multi-objective prediction strategy based on the temporal distribution characteristics assisted by a gated recurrent unit (GRU) neural network (GTBP). First, a time series is created using the improved historical centroid information, and the time series distribution information is represented by the temporal distribution characteristics. Subsequently, a GRU neural network is used to maximize the distribution characteristics and minimize the losses to train the network model. Finally, the individuals composed of the estimated manifold and the population centroids predicted by the model are combined with some individuals randomly generated for increasing the population diversity to form the initial population at the next moment. To evaluate the GTBP performance, it was compared with four dynamic multi-objective algorithms for 15 test problems. The experimental results demonstrated that GTBP is competitive for solving dynamic multi-objective optimization problems.

关键词： Dynamic multi-objective optimization evolutionary algorithms Prediction GRU neural networks Time series

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evolutionary computing for clinical dataset classification using a novel feature selection algorithm

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 5075-5082页

作者： Sheth, Pranali D. Patil, Shrishailappa T. Dhore, Manikrao L. Savitribai Phule Pune Univ Vishwakarma Inst Technol Dept Comp Engn Pune India

The medical diagnostic decision support system uses machine learning and data mining algorithms to detect and diagnose diseases. Several deaths can be avoided if the diseases are detected and cured in the early stages of infection. Feature selection is a major pre-processing method used to obtain the most significant features, thereby enhancing the data mining model's classification accuracy. This work proposes a new feature selection algorithm to perform feature selection as a multi-objective optimization problem. The minimization of classification error rate and minimization of the feature subset's cardinality are the two contradictory objectives that need to be optimized simultaneously. The proposed work is applied for five clinical datasets such as lung cancer, breast cancer, diabetes, fertility, and immunotherapy and the results are compared with existing techniques based on 6 other datasets. This work converts the real-valued Jaya Optimization Algorithm into binary space. It also handles premature convergence and sensitivity-specificity trade-off. The proposed algorithm's efficiency is assessed and analyzed based on average classification accuracy, sensitivity, specificity, number of features selected, percentage feature selection, and CPU computation time. The proposed algorithm improves the effectiveness of data mining based medical diagnostic decision support system.(c) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Classification evolutionary algorithms Feature selection Jaya Optimization Algorithm Multi -objective optimization

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A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

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IEEE ACCESS 2023年 11卷 114270-114282页

作者： Raghul, S. Jeyakumar, G. Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Comp Coimbatore 641112 India

In conventional optimization problems, it is assumed that all relevant parametric constraints remain stationary. In contrast, optimization problems encountered in practical applications are dynamic and supervened by uncertainties. The research community has evinced a keen interest in multi-population approaches combined with nature-inspired algorithms to manage dynamic optimization problems efficiently. Applying multi-population approaches to solve dynamic optimization problems engenders specific vital issues, such as reproducing sub-populations in new environments influenced by archival information. Moreover, over-partitioning the population may lead to aberrant utilization of computational resources among the sub-populations. These impediments are addressed using the proposed hybrid multi-population reinitialization strategy, which is a combination of distributed differential evolution algorithmic framework and re-initialization strategy. This scheme relies on simple reinitialization to surmount the dynamism. This framework was assessed on different instances in a moving peak benchmark problem, a proven benchmarking function in the domain of dynamic optimization. Furthermore, this study encompasses a comparative and statistical analysis to validate the effectiveness of the proposed approach in comparison to cutting-edge algorithms in solving dynamic optimization problems efficiently. The experimental results consistently show that the hybrid multi-population reinitialization strategy outperforms conventional Differential Evolution algorithms across various parameter configurations. This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.

关键词： Dynamic optimization problem evolutionary algorithms differential evolution moving peaks benchmarking problems multi population strategies

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A modified Levy flight distribution for solving high-dimensional numerical optimization problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 204卷 376-400页

作者： He, Quanqin Liu, Hao Ding, Guiyan Tu, Liangping Univ Sci & Technol Liaoning Sch Sci Anshan 114051 Peoples R China

Levy flight distribution is a recent meta-heuristic inspired by levy flight random walk for exploring unknown large search spaces. Similar to other original metaheuristic algorithms, Levy flight distribution can suffer from drawbacks, such as being trapped in minimum local areas and imbalance between the exploitation and exploration. To overcome these weaknesses and enhance the ability of Levy flight distribution in solving high-dimensional numerical optimization problems, a modified Levy flight distribution, called MLFD, is presented. Firstly, Levy flight distribution has good exploration ability;secondly, the symbiosis organisms search has a strong exploitation capability in the mutualism phase. By introducing the mutualism phase, the exploitation ability of the algorithm is improved effectively and help avoid premature convergence. Moreover, a new differential variation strategy is proposed to enhance the diversity of the population and make the algorithm jump out of the local optimum in time. Seventeen well-known high-dimensional unconstrained problems are utilized to compare the proposed algorithm with other nine classical algorithms. The experimental results and statistical analysis demonstrate that MLFD algorithm has promising effectiveness and performance compared with other nine classical algorithms. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： L?vy flight distribution Global optimization evolutionary algorithms Symbiosis organisms search

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Domination-Based Selection and Shift-Based Density Estimation for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2023年第4期27卷 993-1004页

作者： Zhou, Jinlong Zhang, Yinggui Zheng, Jinhua Li, Miqing Cent South Univ Sch Traff & Transport Engn Changsha 410075 Peoples R China Xiangtan Univ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Univ Birmingham Ctr Excellence Res Computat Intelligence & Applica Sch Comp Sci Birmingham B15 2TT England

Balancing constraints and objective functions in constrained evolutionary multiobjective optimization is not an easy task. Overemphasis on constraints satisfaction may easily lead to the search to get stuck in local optimal regions, and overemphasis on objectives optimization may lead to substantial search resources wasted on infeasible regions. This article proposes a constrained multiobjective optimization algorithm, called CMOEA-SDE, aiming to achieve a good balance between the above two issues. To do so, CMOEA-SDE presents a strictly constrained dominance relation and a constrained shift-based density estimation strategy. Specifically, the former defines a new dominance relation that considers both constraint satisfaction and the objective functions. It favors good feasible solutions but still leaves room for infeasible solutions to be selected. Unlike most density estimation methods, which only consider the diversity of solutions, our shift-based density estimator covers both the feasibility and the diversity of solutions. That is, our estimator shifts the solutions' positions based on the extent of the constraints they violate so that solutions violating constraints more severely are shifted to crowded areas, thus being eliminated early. Systematic experiments were conducted on four benchmark test suites and six real-world constrained multiobjective optimization problems. The experimental results suggest that the proposed algorithm can achieve very competitive performance against state-of-the-art constrained multiobjective evolutionary algorithms.

关键词： Balance between objectives and constraints con-strained optimization evolutionary algorithms multiobjective optimization

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A Constrained Multi-Objective evolutionary Algorithm Based on Early Convergence Followed by Diversity 5

A Constrained Multi-Objective Evolutionary Algorithm Based o...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Li, Wenji Qiu, Yifeng Wang, Zhaojun Zhang, Qinchang Mai, Ruitao Xu, Biao Zhang, Yue Zhuang, Jiafan Fan, Zhun Shantou University Guangdong China

ISBN: (纸本)9798350393521

Many real-world optimization problems can be formulated as a kind of constrained multi-objective optimization problems (CMOPs). The main difficulty in solving these problems is to take feasibility, convergence and diversity into account simultaneously. To address this issue, this paper proposes a push and pull search algorithm based on early convergence followed by diversity (PPS-CFD). The proposed algorithm is composed of three different stages, each respectively focusing on convergence, diversity and feasibility. In the first stage, the population rapidly converges to the unconstrained Pareto front (UPF) in M directions, where M is the number of objectives of CMOPs. In the second stage, the population further converges to the UPF and meanwhile its diversity is enhanced. In the last stage, constraints are taken into account to pull the population from the UPF to the constrained Pareto front (CPF). In addition, a search strategy based on objective space division is proposed at the last two stages. Finally, the proposed PPS-CFD is tested on fourteen benchmark problems, compared with other six algorithms, to demonstrate its superiority. © 2023 IEEE.

关键词： evolutionary algorithms

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Minimizing Expected Deviation in Upper Level Outcomes Due to Lower Level Decision Making in Hierarchical Multiobjective Problems

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2023年第3期27卷 505-519页

作者： Deb, Kalyanmoy Lu, Zhichao Kropp, Ian Hernandez-Suarez, J. Sebastian Hussein, Rayan Miller, Steven Nejadhashemi, A. Pouyan Michigan State Univ Coll Engn E Lansing MI 48824 USA Michigan State Univ Coll Agr & Nat Sci E Lansing MI 48824 USA

Many societal and industrial problem-solving tasks involving search, optimization, design, and management are conveniently decomposed into hierarchical subproblems. While this process allows a systematic procedure to have a multistakeholder solution, the independent decision-making process for the lower level problem causes a deviation in the expected outcome of the upper level problem. In this article, we provide a new and computationally efficient evolutionary approach allowing upper level decision makers to analyze the vagaries of lower level decision making when choosing a preferred solution with the minimum deviation from their expectations. This concept is novel and pragmatic. We demonstrate the concept through a search for optimistic-pessimistic tradeoff solutions found by an evolutionary multiobjective optimization approach first on two difficult test problems, then on a watershed management problem and a telecommunication management problem. The approach is generic and can be applied to similar hierarchical management problems to achieve minimum deviation with a more predictive and reliable outcome. The proposed solution procedure is found to choose an optimistic solution that has approximately 31%-65% reduced deviation compared to another optimistic solution chosen at random in the test problems and approximately 85%-95% reduced deviation in the two practical problems, making the method of this study applicable to practical hierarchical problems.

关键词： Optimization Decision making Optimized production technology Task analysis Systematics Search problems Process control Bilevel optimization decision making evolutionary algorithms hierarchical optimization

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Hierarchical structure-based joint operations algorithm for global optimization

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SWARM AND evolutionary COMPUTATION 2023年 79卷

作者： Sun, Gaoji Han, Rongqing Deng, Libao Li, Chunlei Yang, Guoqing Zhejiang Normal Univ Coll Econ & Management Jinhua Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin Peoples R China Hebei Univ Sch Management Baoding Peoples R China

Joint operations algorithm (JOA) is a metaheuristic algorithm based on joint operations strategy in military theory, which incorporates three core operations - offensive, defensive and regroup - and has excellent performance in global optimization problems. To enhance the optimization performance of the original JOA, we re-examine the positioning of the three core operations in balancing global exploration and local exploitation, and propose a hierarchical structure-based JOA variant (abbreviated as HSJOA) by adjusting their execution mechanism. In addition, we redesign the three core operations to give full play to their synergy effect. In the new offensive operations, we simplify its specific execution strategy, but introduce an adjustment parameter to retain valuable position information. In the modified defensive operations, we integrate the influence of officer and elite comrades on soldiers to design a Gaussian distribution-based conservative defensive strategy and one Cauchy distribution-based aggressive defensive strategy. In the novel regroup operations, we replace the original random division strategy with a sorting-based division scheme. To evaluate the optimization performance of HSJOA, we conduct comparison experiments using nine excellent algorithms to deal with four real-life optimization problems and thirty test functions from IEEE CEC 2014 testbed. The comparison results show that the overall optimization performance of HSJOA is significantly better than the original JOA, the outstanding variants (TAPSO and GODE) of two well-known algorithms and four recently proposed algorithms (EO, SOA, MPA and WOA), while inferior to two winners of IEEE CEC competition (L-SHADE and EBOwithCMAR), but HSJOA clearly outperforms L-SHADE and EBOwithCMAR in terms of the runtime consumption.

关键词： Joint operations algorithm Metaheuristic algorithms Swarm intelligence algorithms evolutionary algorithms Global optimization

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