In order to improve algorithm performance and reduce its time complexity, this paper proposes an efficient multi-objective state transition algorithm based on the improved crowding distance (EMOSTA_ICD). This algorith...
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In order to improve algorithm performance and reduce its time complexity, this paper proposes an efficient multi-objective state transition algorithm based on the improved crowding distance (EMOSTA_ICD). This algorithm combines the rotation operator, expansion operator, axesion operator from the state transition algorithm (STA), along with the translation operator, to guide the evolution process and quickly generate high-quality candidate solutions. Furthermore, an efficient non-dominated sorting based on dynamical resource allocation (ENS-DRA) is developed to select candidate solutions, thus reducing computational complexity by excluding "poor offspring" from the non-dominated sorting. Additionally, an improved crowding distance (ICD) that considers the quality of candidate solutions is designed to maintain population diversity while considering evolutionary potential. To validate its effectiveness, the proposed algorithm is tested on 21 benchmark functions, an engineering optimization problem in the hydrometallurgy process, and a four-bar truss design optimization problem. It is also compared with 11 advanced multi-objective evolutionary algorithms (MOEAs). The results demonstrate that EMOSTA_ICD performs well on most test problems and efficiently solves practical engineering problems. It exhibits outstanding reliability, practicality, and competitiveness.
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 promisi...
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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.
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 applicabili...
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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.
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 distributo...
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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.
Clustering problems widely exist in machine learning, pattern recognition, image analysis and information sciences, etc. Although many clustering algorithms have been proposed, it is unpractical to find a clustering a...
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Clustering problems widely exist in machine learning, pattern recognition, image analysis and information sciences, etc. Although many clustering algorithms have been proposed, it is unpractical to find a clustering algorithm suitable for all types of datasets. Fuzzy c-means (FCM) is one of the most frequently-used fuzzy clustering algorithm for the reason that it is efficient, straightforward, and easy to implement. However, the traditional FCM taking Euclidean distance as similarity measurement can not distinguish the intersection between two clusters. Therefore, kernel function has been taken as similarity measurement to solve this issue. As a comprehensive partition criterion, intuitionistic fuzzy set which consider both membership degree and non-membership degree has been used to replace traditional fuzzy set to describe the natural attributes of objective phenomena more delicately. Thus, Kernel intuitionistic fuzzy c-means (KIFCM) has been proposed in this paper to settle clustering problem. Considering FCM is easily getting trapped in local optima due to its high sensitivity to initial centroid. state transition algorithm (STA) has been adopted in this study to obtain the initial centroid to enhance its stability. The proposed STA-KIFCM compared with some other clustering algorithms are implemented using five benchmark datasets. Experimental results not only show that the proposed method is efficient and can reveal encouraging results, but also indicate that the proposed method can achieve high accuracy.
Influence maximization problem is the procedure of attempting to identify a group of K nodes in a social network in order to maximize the dissemination of influence under certain influence models. Based on state trans...
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Influence maximization problem is the procedure of attempting to identify a group of K nodes in a social network in order to maximize the dissemination of influence under certain influence models. Based on state transition algorithm (STA) and a multiple criteria decision making (MCDM) method called Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), a novel hybrid approach has been proposed to cope with the influence maximization problem in this paper. Firstly, an intelligent optimization paradigm called STA is introduced to obtain the most appropriate weights that are used to integrate the criteria of each alternative in the VIKOR method. Then, a hybrid normalization technique has been presented to allow the process of aggregating criterion with numerical and comparable data properly in this method. Several typical networks have been used to testify the effectiveness of proposed method and technique. Compared with other approaches, experimental results show that our approach can solve the influence maximization problem more effectively. (C) 2020 Elsevier B.V. All rights reserved.
The nonferrous metallurgy industry is a major energy consumer in China, and accurate energy consumption forecasting for the nonferrous metallurgy industry can help government policymakers with energy planning. For thi...
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The nonferrous metallurgy industry is a major energy consumer in China, and accurate energy consumption forecasting for the nonferrous metallurgy industry can help government policymakers with energy planning. For this purpose, a hybrid support vector regression (HSVR) with an adaptive state transition algorithm (ASTA) named ASTA-HSVR is proposed to forecast energy consumption in the nonferrous metallurgy industry. The proposed support vector regression (SVR) model consists of a linear weighting of epsilon-SVR and nu-SVR. The ASTA was developed to optimize the parameters of the HSVR. Two cases of energy consumption from the nonferrous metallurgy industry in China are used to demonstrate the performance of the proposed method. The results indicate that the ASTA-HSVR method is superior to other methods. In this study, a hybrid support vector regression with an adaptive state transition algorithm (ASTA-HSVR) was developed and successfully applied to energy consumption forecasting for the nonferrous metallurgy industry. However, it should be noted that the outliers were not considered in this study. In the future, we expect to extend the ASTA-HSVR method to include energy consumption forecasting problems with outliers.
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. Howev...
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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.
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 ...
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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.
Parameter Optimal state transition algorithm (POSTA) is a global metaheuristic method with adaptive parameter adjustment capabilities. However, its fixed local search candidate solution space easily leads to the compu...
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ISBN:
(纸本)9781728176871
Parameter Optimal state transition algorithm (POSTA) is a global metaheuristic method with adaptive parameter adjustment capabilities. However, its fixed local search candidate solution space easily leads to the computational redundancy and the problems that are difficult to jump out of local optimization. Therefore, a novel state transition algorithm with variable local candidate solution space is proposed in this paper, called VLCSSSTA. First, a dynamic logic of distance control is designed to speed up the search, which adaptively changes the size of the candidate solution space. Then, the larger candidate solutions are randomly generated to enhance the ability to jump out of the local optimum. Statistical research results show that the proposed algorithm has better performance than POSTA and some classic optimization algorithms in five benchmark function experiment. Finally, the proposed method is applied to the operation optimization in residue hydrogenation fractionation (RHF). The experimental results also verify the effectiveness of the proposed method.
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