arXiv subjects

Xiaojun Zhou

Publications and source records attributed to Xiaojun Zhou.

At least 19 recordsLinked to original sources

arXiv2026

Efficient state transition algorithm with guaranteed optimality

The state transition algorithm (STA), as an intelligent optimization method grounded in constructivist learning, has been demonstrated to be highly effective in solving complex optimization problems. However, the standard STA suffers from slow convergence, particularly in the later stages when dealing with flat landscapes. Additionally, users are required to set the maximum number of iterations based on intuition. To address these issues, an enhanced STA with guaranteed optimality is introduced. This improvement involves three key components. First, novel translation transformations, inspired by predictive modeling, are developed to generate a broader set of candidate solutions by leveraging historical data. Second, adaptive parameter control strategies are incorporated to accelerate convergence. Finally, a dedicated termination condition is designed to ensure that the algorithm converges at the optimal solution, analogous to the zero gradient condition in mathematical programming. The comprehensive experimental results validate the effectiveness and superiority of the proposed method. The source codes for ESTA and EXSTA will be publicly available at: https://github.com/tiezhongyu2005/ESTA.

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arXiv2025

Stagnation in Evolutionary Algorithms: Convergence $\neq$ Optimality

In the evolutionary computation community, it is widely believed that stagnation impedes convergence in evolutionary algorithms, and that convergence inherently indicates optimality. However, this perspective is misleading. In this study, it is the first to highlight that the stagnation of an individual can actually facilitate the convergence of the entire population, and convergence does not necessarily imply optimality, not even local optimality. Convergence alone is insufficient to ensure the effectiveness of evolutionary algorithms. Several counterexamples are provided to illustrate this argument.

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arXiv2022

Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media

Automatic detecting rumors on social media has become a challenging task. Previous studies focus on learning indicative clues from conversation threads for identifying rumorous information. However, these methods only model rumorous conversation threads from various views but fail to fuse multi-view features very well. In this paper, we propose a novel multi-view fusion framework for rumor representation learning and classification. It encodes the multiple views based on Graph Convolutional Networks (GCN), and leverages Convolutional Neural Networks (CNN) to capture the consistent and complementary information among all views and fuse them together. Experimental results on two public datasets demonstrate that our method outperforms state-of-the-art approaches.

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arXiv2021

A Multiobjective State Transition Algorithm Based on Decomposition

Aggregation functions largely determine the convergence and diversity performance of multi-objective evolutionary algorithms in decomposition methods. Nevertheless, the traditional Tchebycheff function does not consider the matching relationship between the weight vectors and candidate solutions. In this paper, the concept of matching degree is proposed which employs vectorial angles between weight vectors and candidate solutions. Based on the matching degree, a new modified Tchebycheff aggregation function is proposed, which integrates matching degree into the Tchebycheff aggregation function. Moreover, the proposed decomposition method has the same functionality with the Tchebycheff aggregation function. Based on the proposed decomposition approach, a new multiobjective optimization algorithm named decomposition-based multi-objective state transition algorithm is proposed. Relevant experimental results show that the proposed algorithm is highly competitive in comparison with other state-of-the-art multiobjetive optimization algorithms.

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arXiv2021

A benchmark generator for boolean quadratic programming

For boolean quadratic programming (BQP), we will show that there is no duality gap between the primal and dual problems under some conditions by using the classical Lagrangian duality. A benchmark generator is given to create random BQP problems which can be solved in polynomial time. Several numerical examples are generated to demonstrate the effectiveness of the proposed method.

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arXiv2021

Multiagent based state transition algorithm for global optimization

In this paper, a novel multiagent based state transition optimization algorithm with linear convergence rate named MASTA is constructed. It first generates an initial population randomly and uniformly. Then, it applies the basic state transition algorithm (STA) to the population and generates a new population. After that, It computes the fitness values of all individuals and finds the best individuals in the new population. Moreover, it performs an effective communication operation and updates the population. With the above iterative process, the best optimal solution is found out. Experimental results based on some common benchmark functions and comparison with some stat-of-the-art optimization algorithms, the proposed MASTA algorithm has shown very superior and comparable performance.

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arXiv2020

An ensemble learning framework based on group decision making

The classification problem is a significant topic in machine learning which aims to teach machines how to group together data by particular criteria. In this paper, a framework for the ensemble learning (EL) method based on group decision making (GDM) has been proposed to resolve this issue. In this framework, base learners can be considered as decision-makers, different categories can be seen as alternatives, classification results obtained by diverse base learners can be considered as performance ratings, and the precision, recall, and accuracy which can reflect the performances of the classification methods can be employed to identify the weights of decision-makers in GDM. Moreover, considering that the precision and recall defined in binary classification problems can not be used directly in the multi-classification problem, the One vs Rest (OvR) has been proposed to obtain the precision and recall of the base learner for each category. The experimental results demonstrate that the proposed EL method based on GDM has higher accuracy than other 6 current popular classification methods in most instances, which verifies the effectiveness of the proposed method.

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arXiv2018

A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm

State transition algorithm (STA) has been emerging as a novel metaheuristic method for global optimization in recent few years. In our previous study, the parameter of transformation operator in continuous STA is kept constant or decreasing itself in a periodical way. In this paper, the optimal parameter selection of the STA is taken in consideration. Firstly, a statistical study with four benchmark two-dimensional functions is conducted to show how these parameters affect the search ability of the STA. Based on the experience gained from the statistical study, then, a new continuous STA with optimal parameters strategy is proposed to accelerate its search process. The proposed STA is successfully applied to twelve benchmarks with 20, 30 and 50 dimensional space. Comparison with other metaheuristics has also demonstrated the effectiveness of the proposed method.

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arXiv2016

A Comparative Study of STA on Large Scale Global Optimization

State transition algorithm has been emerging as a new intelligent global optimization method in recent few years. The standard continuous STA has demonstrated powerful global search ability for global optimization problems whose dimension is no more than 100. In this study, we give a test report to present the performance of standard continuous STA for large scale global optimization when compared with other state-of-the-art evolutionary algorithms. From the experimental results, it is shown that the standard continuous STA still works well for almost all of the test problems, and its global search ability is much superior to its competitors.

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arXiv2016

A Rolling PID Control Approach and its Applications

The canonical proportional-integral-derivative (PID) control approach has been widely used in industrial application due to their simplicity and ease of use. However, its corresponding controller parameters are hard to be adjusted, especially for nonlinear systems. The optimization-based method provides a general framework to find optimal PID controller parameters; nevertheless, several disadvantages exist, for example, it is nontrivial to select an appropriate sample size and it is necessary to obtain the global optimal solution but the optimization problem is non-convex, making it hard to achieve. To alleviate the aforementioned limitations, a rolling PID control approach is proposed in this study, in which, at each rolling period, the PID controller parameters are updated using observable data, which can be classified to data-driven control method. The effectiveness of the proposed approach has been validated by experiments.

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arXiv2016

A matlab toolbox for continuous state transition algorithm

State transition algorithm (STA) has been emerging as a novel stochastic method for global optimization in recent few years. To make better understanding of continuous STA, a matlab toolbox for continuous STA has been developed. Firstly, the basic principles of continuous STA are briefly described. Then, 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 constraints. In the same while, a link is provided to download the matlab toolbox via available resources.

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arXiv2015

Nonlinear system identification and control using state transition algorithm

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance.

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arXiv2015

Discrete State Transition Algorithm for Unconstrained Integer Optimization Problems

A recently new intelligent optimization algorithm called discrete state transition algorithm is considered in this study, for solving unconstrained integer optimization problems. Firstly, some key elements for discrete state transition algorithm are summarized to guide its well development. Several intelligent operators are designed for local exploitation and global exploration. Then, a dynamic adjustment strategy ``risk and restoration in probability" is proposed to capture global solutions with high probability. Finally, numerical experiments are carried out to test the performance of the proposed algorithm compared with other heuristics, and they show that the similar intelligent operators can be applied to ranging from traveling salesman problem, boolean integer programming, to discrete value selection problem, which indicates the adaptability and flexibility of the proposed intelligent elements.

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arXiv2015

A dynamic state transition algorithm with application to sensor network localization

The sensor network localization (SNL) problem is to reconstruct the positions of all the sensors in a network with the given distance between pairs of sensors and within the radio range between them. It is proved that the computational complexity of the SNL problem is NP-hard, and semi-definite programming or second-order cone programming relaxation methods are only able to solve some special problems of this kind. In this study, a stochastic global optimization method called the state transition algorithm is introduced to solve the SNL problem without additional assumptions and conditions of the problem structure. To transcend local optimality, a novel dynamic adjustment strategy called "risk and restoration in probability" is incorporated into the state transition algorithm. An empirical study is investigated to appropriately choose the "risk probability" and "restoration probability", yielding the dynamic state transition algorithm, which is further improved by gradient-based refinement. The dynamic state transition algorithm with refinement is applied to the SNL problem, and satisfactory experimental results have testified the effectiveness of the proposed approach.

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arXiv2013

State Transition Algorithm

In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.

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arXiv2013

Optimal Design of Water Distribution Networks by Discrete State Transition Algorithm

Optimal design of water distribution networks, which are governed by a series of linear and nonlinear equations, has been extensively studied in the past decades. Due to their NP-hardness, methods to solve the optimization problem have changed from traditional mathematical programming to modern intelligent optimization techniques. In this study, with respect to the model formulation, we have demonstrated that the network system can be reduced to the dimensionality of the number of closed simple loops or required independent paths, and the reduced nonlinear system can be solved efficiently by the Newton-Raphson method. Regarding the optimization technique, a discrete state transition algorithm (STA) is introduced to solve several cases of water distribution networks. In discrete STA, there exist four basic intelligent operators, namely, swap, shift, symmetry and substitute as well as the "risk and restore in probability" strategy. Firstly, we focus on a parametric study of the restore probability $p_1$ and risk probability $p_2$. To effectively deal with the head pressure constraints, we then investigate the effect of penalty coefficient and search enforcement on the performance of the algorithm. Based on the experience gained from the training of the Two-Loop network problem, the discrete STA has successfully achieved the best known solutions for the Hanoi and New York problems. A detailed comparison of our results with those gained by other algorithms is also presented.

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arXiv2013

A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem

Generalized traveling salesman problem (GTSP) is an extension of classical traveling salesman problem (TSP), which is a combinatorial optimization problem and an NP-hard problem. In this paper, an efficient discrete state transition algorithm (DSTA) for GTSP is proposed, where a new local search operator named \textit{K-circle}, directed by neighborhood information in space, has been introduced to DSTA to shrink search space and strengthen search ability. A novel robust update mechanism, restore in probability and risk in probability (Double R-Probability), is used in our work to escape from local minima. The proposed algorithm is tested on a set of GTSP instances. Compared with other heuristics, experimental results have demonstrated the effectiveness and strong adaptability of DSTA and also show that DSTA has better search ability than its competitors.

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arXiv2013

A Multiobjective State Transition Algorithm for Single Machine Scheduling

In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm.

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