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第三十一届中国控制会议

作者： YANG Chunhua~1,TANG Xiaolin~1,ZHOU Xiaojun~(1,2),GUI Weihua~1 1.School of Information Science and Engineering,Central South University,Changsha 410083,P.R.China 2.School of Science,Information Technology and Engineering,University of Ballarat,Victoria 3353,Australia

Discrete version of state transition algorithm is proposed in order to solve the traveling salesman *** special operators for discrete optimization problem named swap,shift and symmetry transformations are *** analysis and time complexity of the algorithm are also *** make the algorithm simple and efficient,no parameter adjusting is suggested in current *** are carried out to test the performance of the strategy,and comparisons with simulated annealing and ant colony optimization have demonstrated the effectiveness of the proposed *** results also show that the discrete state transition algorithm consumes much less time and has better search ability than its counterparts, which indicates that state transition algorithm is with strong adaptability.

关键词： state transition algorithm Traveling Salesman Problem Swap Shift Symmetry

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A novel state transition algorithm with adaptive fuzzy penalty for multi-constraint UAV path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Zhou, Xiaojun Tang, Zhouhang Wang, Nan Yang, Chunhua Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Texas A&M Univ Qatar Doha Qatar

Unmanned aerial vehicles (UAVs) require pre-planned flight paths that are energy-efficient, safe and smooth across their wide range of application scenarios. In this study, a novel UAV path planning method is proposed. Firstly, the UAV path planning under numerous obstacles is modeled as a continuous constrained optimization problem. The cost function is formulated as a linear combination of length, height variation, and smoothness, while the constraints include obstacle avoidance, height limitation, and the maneuverability of UAV. Subsequently, a novel constrained state transition algorithm with adaptive fuzzy penalty (AFSTA) is proposed to solve the optimization problem. In AFSTA, a novel adaptive fuzzy penalty function is designed to leverage expert knowledge to establish a reasonable mapping relationship from the fitness value and the degree of constraint violation to the penalty factor for a candidate solution. Meanwhile, the state transition algorithm (STA) is used as the search engine for both global and local search. Experimental results illustrate that the proposed method can find energy-efficient, safe, and maneuverable flight paths successfully with the superiority over other state -of -the -art metaheuristic algorithms.

关键词： Unmanned aerial vehicle Path planning state transition algorithm Adaptive fuzzy penalty

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An interactive feature selection method based on multi-step state transition algorithm for high-dimensional data

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KNOWLEDGE-BASED SYSTEMS 2023年 282卷

作者： Du, Yangyi Zhou, Xiaojun Yang, Chunhua Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 10587 Qatar

Feature selection (FS) has been extensively employed in classification tasks to reduce data dimensionality and enhance prediction performance effectively. Recently, hybrid filter-wrapper methods have exhibited promising results in FS problems by leveraging both advantages. However, the inadequate integration of the filter method into the wrapper method leads to the hybrid algorithms exhibiting poor efficiency in classification as datasets grow in complexity. In this paper, an interactive feature selection framework based on state transition algorithm (STA) is proposed to address high-dimensional FS problems. In this framework, prior knowledge of the features is formed via the mutual information-based filter method. The STA is a powerful search engine that traverses the feature space in the wrapper stage. Moreover, an external trainer with prior knowledge will guide the exploration direction of STA in a simple but efficient way to accelerate the search process. And a self-adaptive mechanism is proposed to adjust the prior knowledge during the search process. Specifically, the external trainer establishes the interactive loop, while the self-adaptive mechanism aims to feed feature information back to the loop. In addition, the search process is prevented from being trapped in local optima by employing a multi-step STA, which allows for continuous transformations of the solution in probability. Finally, the proposed FS method is applied to various public classification datasets. The experimental results demonstrate that the proposed method is a highly competitive FS method, outperforming several state-of-the-art algorithms in generating an optimal subset of features.

关键词： Feature selection state transition algorithm Interactive learning Mutual information

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Nonlinear bilevel programming approach for decentralized supply chain using a hybrid state transition algorithm

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KNOWLEDGE-BASED SYSTEMS 2022年 240卷 108119-108119页

作者： Zhou, Xiaojun Tian, Jituo Wang, Zeyu Yang, Chunhua Huang, Tingwen Xu, Xuesong Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha 23874 Qatar Hunan Univ Technol & Business Sch Frontier Crossover Studies Changsha 410205 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

This paper investigates a decentralized supply chain that is composed of one manufacturer and multiple distributors. The manufacturer produces goods and wholesales them to multiple distributors and then the distributors sell products to various markets. The entire production period of the manufacturer is divided into several intervals. The decision-making problem of the entire decentralized supply chain is presented as a two-echelon coordination game network, in which each decision-maker can influence decision-making of other levels. A Stackelberg game framework is proposed to coordinate the decision-making process. And then two nonlinear bi-level programming (BLP) models are developed to find the optimal equilibrium decision scheme by switching the leader and follower roles between the manufacturer and the distributors. The models consider the manufacturer's budget constraints in each interval and the market demands are affected by distributors' selling price and advertising strategies. According to the hierarchy and complexity of bi-level programming problem (BLPP), a nested bi-level method based on hybrid state transition algorithm is proposed to address the BLP models, and mapping approximation strategy is utilized to improve computational efficiency. Finally, the numerical experiments are performed to demonstrate the superiority of the proposed method in terms of accuracy and computational efficiency. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Decentralized supply chain Nonlinear bilevel programming state transition algorithm Mapping approximation

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A multiobjective state transition algorithm based on modified decomposition method

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APPLIED SOFT COMPUTING 2022年第0期119卷 108553-108553页

作者： Zhou, Xiaojun Gao, Yuan Yang, Shengxiang Yang, Chunhua Zhou, Jiajia Cent South Univ Sch Automat Changsha 410083 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE19BH England Peng Cheng Lab Shenzhen 518000 Peoples R China

Aggregation functions largely determine the convergence and diversity performance of multi-objective algorithms in decomposition methods. Nevertheless, the traditional Tchebycheff function does not consider the matching relationship between the weight vectors and candidate solutions. To deal with this issue, a new multiobjective state transition algorithm based on modified decomposition method (MOSTA/D) is proposed. According to the analysis of the relationship between the weight vectors and candidate solutions under the Tchebycheff decomposition scheme, the concept of matching degree is introduced which employs vectorial angles between weight vectors and candidate solutions. Based on the matching degree, a new modified Tchebycheff aggregation function is proposed in MOSTA/D. It can adaptively select the candidate solutions which are better matched with the weight vectors. This proposed MOSTA/D decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them in a collaborative manner. Each individual solution in the population of MOSTA/D is associated with a subproblem. Four mutation operators in STA are adopted to generating candidate solutions on subproblems and maintaining the population diversity. Relevant experimental results show that the proposed algorithm is highly competitive in comparison with other state-of-the-art evolutionary algorithms on tackling a set of benchmark problems with complicated Pareto fronts and a typical engineering optimization problem. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Decomposition method Matching degree Tchebycheff approach state transition algorithm

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Unlocking operational excellence: A deep dive into a communication-driven multi-strategy state transition algorithm for industrial process optimization

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年第1期240卷

作者： Tan, Xujie Wang, Yalin Liu, Chenliang Yuan, Xiaofeng Wang, Kai Cent South Univ Sch Automat Changsha 410083 Peoples R China

The optimization of industrial processes is crucial for enhancing operational safety, productivity, and energy efficiency. However, the increasing complexity of industrial processes poses challenges to the applicability of intelligent optimization algorithms, such as the state transition algorithm. The main challenges include limited individual diversity, the intricate task of escaping local optima, and the underutilization of solutions beyond predefined boundaries. To overcome these challenges, this study proposes a novel intelligent stochastic optimization algorithm called the communication-driven multi-strategy state transition algorithm (CDMS-STA). CDMS-STA incorporates three selfdesigned strategies: communication, self-learning, and cross-boundary solution processing. Specifically, the communication strategy enhances the diversity of candidate solutions by promoting information exchange and sharing among individuals. The self-learning strategy facilitates adaptive adjustments based on historical information. The cross-boundary solution processing strategy maximizes the utilization of solutions beyond defined boundaries. To evaluate the effectiveness of the proposed algorithm, extensive experiments were conducted on a numerical sample case and two chemical process cases. The experimental results demonstrate that CDMS-STA outperforms several state-of-the-art optimization methods to find the optimal solution, which also validates the potential of the proposed algorithm for practical application in industrial process optimization.

关键词： Industrial processes Operational optimization Communication-driven multi-strategy optimi zation state transition algorithm

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An adaptive state transition algorithm with local enhancement for global optimization

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APPLIED SOFT COMPUTING 2022年 121卷 108733-108733页

作者： Dong, Yingchao Zhang, Hongli Wang, Cong Zhou, Xiaojun Xinjiang Univ Sch Elect Engn Urumqi 830047 Xinjiang Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

state transition algorithm (STA) is an efficient and powerful metaheuristic method for solving global optimization problems, and it has been successfully applied in many engineering fields in the past few years. However, the basic STA has weak local search capability and shows slow convergence rate and low convergence accuracy in the later search stage. In view of the above shortcomings, an adaptive state transition algorithm (ASTA) with local enhancement is proposed in this paper. Firstly, the order of using state transformation operators and the optimal parameters of the operators are considered in each iteration of ASTA, and a statistical method is employed to adaptively select the optimal transformation operator and the parameter values of the optimal operator to speed up the search process. Then, an adaptive call strategy is adopted to determine its convergence to the neighborhood of the optimal solution and to decide whether to perform the quasi-Newton operator for local enhancement. Finally, the degree to which the current solution is close to the optimal solution is judged by the information of historical solutions, and an analytical solution is quickly obtained by calling the quadratic interpolation operator. The effectiveness of the proposed ASTA is checked, through a comparison with other metaheuristic methods, on 15 benchmark functions and several real-world optimization problems. Experimental results show that ASTA has a stronger search capability than the basic STA, STA variants, and some state-of-the-art metaheuristic methods. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Global optimization state transition algorithm Adaptive strategy Local enhancement Quasi-Newton Quadratic interpolation

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Evaluation of Wireless Sensor Networks Based on state transition algorithm and Fuzzy TOPSIS

Evaluation of Wireless Sensor Networks Based on State Transi...

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第34届中国控制与决策会议

作者： Wan Tan Xiaojun Zhou Yan Sun School of Automation Central South University

Many exsting interval-valued intuitionistic fuzzy multiattribute decision making methods in evaluating wireless sensor networks(WSNs) are baesd on real-valued measures,which may lose divergence information and affect the accuracy of decision *** this paper,a novel similarity-divergence measure of interval-valued intuitionistic fuzzy values is proposed,which can overcome the drawbacks of real-valued measures,and the rationality of the proposed measure is further *** measure is applied to calculate fuzzy similarities of each alternative and a TOPSIS method is developed to obtain the preference order of the *** the different importance of each attribute,a nonlinear optimization model is constructed,and the state transition algorithm(STA) is used to determine the attribute ***,the proposed method is used to evaluate the designed *** experimental results verify the effectiveness and superiority of the proposed approach in solving multiattribute decision making problems.

关键词： Wireless sensor network Performance evaluation Similarity-divergence measure Interval-valued intuitionistic fuzzy set state transition algorithm

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A Discrete Multi-objective state transition algorithm for Flow Rate Sensors Placement Problem in Wastewater Treatment Plants

A Discrete Multi-objective State Transition Algorithm for Fl...

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第32届中国过程控制会议(CPCC2021)

作者： Wenting Li Jie Han Yonggang Li School of Automation Central South University

With the increasing quantity and importance of wastewater treatment plants,how to obtain all-round and high-quality data to ensure the stable and efficient operation of the plants draws more and more *** order to achieve an organic balance between information richness and cost,this paper develops a discrete multi-objective optimization method based on the state transition algorithm to arrange flow rate sensors in the whole ***,an industrial condition weight factors design method based on the triangular fuzzy analytic hierarchy process is introduced to assign weight factors of flows in different processing ***,drawing support from the graph theory and structural observability and redundancy criteria,the sensor optimization model is established in terms of sensor cost,variable observability,and sensor redundancy,which transforms the optimal flow rate sensors placement problem to a discrete multi-objective optimization ***,a novel metaheuristic method named discrete multi-objective state transition algorithm is proposed to obtain the Pareto front of the optimization ***,an evaluation strategy is proposed to select the best solution for further industrial *** municipal wastewater treatment plants with different configurations are used to illustrate the effectiveness and advantage of the proposed optimization method,which provides a more cost-efficient flow rate sensors placement scheme for plant upgrade or new plant design.

关键词： Wastewater treatment Sensor placement Weighted flows Multi-objective optimization state transition algorithm Decision making method

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A matlab toolbox for continuous state transition algorithm

A matlab toolbox for continuous state transition algorithm

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第35届中国控制会议

作者： Xiaojun Zhou Chunhua Yang Weihua Gui School of Information Science and Engineering Cental South University

ISBN: (纸本)9781509009107

state transition algorithm(STA) has been emerging as a novel stochastic method for global optimization in recent few *** make better understanding of continuous STA,a matlab toolbox for continuous STA has been ***,the basic principles of continuous STA are briefly ***,a matlab implementation of the standard continuous STA is explained,with several instances given to show how to use to the matlab toolbox to minimize an optimization problem with bound *** the same while,a link is provided to download the matlab toolbox via available resources.

关键词： state transition algorithm matlab toolbox global optimization continuous optimization

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