Papers by Shengxiang Yang
Soft Computing, Nov 19, 2015
Train rescheduling after a perturbation is a challenging task and is an important concern of the ... more Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it Communicated by D. Neagu.
International Journal of Hybrid Intelligent Systems, Jun 17, 2011
Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation d... more Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator. GES CES qGES Experiment τ Median Mean STD Median Mean STD Median Mean
In this paper, the effect of the population size on the performance of the MAX-MIN ant system for... more In this paper, the effect of the population size on the performance of the MAX-MIN ant system for dynamic optimization problems (DOPs) is investigated. DOPs are generated with the dynamic benchmark generator for permutationencoded problems. In particular, the empirical study investigates: a) possible dependencies of the population size parameter with the dynamic properties of DOPs; b) the effect of the population size with the problem size of the DOP; and c) whether a larger population size with less algorithmic iterations performs better than a smaller population size with more algorithmic iterations given the same computational budget for each environmental change. Our study shows that the population size is sensitive to the magnitude of change of the DOP and less sensitive to the frequency of change and the problem size. It also shows that a longer duration in terms of algorithmic iterations results in a better performance.
This paper proposes a self-adaptation method to control not only the mutation strength parameter,... more This paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic q-Gaussian distribution is employed in the mutation operator. The q-Gaussian distribution allows to control the shape of the distribution by setting a real parameter q and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the q-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm.
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IEEE Computational Intelligence Magazine, Feb 1, 2020
Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior o... more Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.
Institution of Engineering and Technology eBooks, Sep 29, 2018
The ant colony optimization (ACO) meta-heuristic was inspired from the foraging behaviour of real... more The ant colony optimization (ACO) meta-heuristic was inspired from the foraging behaviour of real ant colonies. In particular, real ants communicate indirectly via pheromone trails and find the shortest path. Although real ants proved that they can find the shortest path when the available paths are known a prior, they may face serious challenges when some paths are made available after the colony has converged to a path. This is because the colony may continue to follow the current path rather than exploring the new paths in case a shorter path is available. For the ACO meta-heuristic, the challenges are similar when applied to dynamic optimization problems (DOPs). Once the algorithm converges, it loses its adaptation capabilities and may have poor performance in DOPs. Several strategies have been integrated with ACO to address difficult combinatorial DOPs. Their performance proved that ACO is a powerful computational technique for combinatorial DOPs once enhanced. This chapter investigates the applications of ACO for combinatorial DOPs.
Lecture Notes in Computer Science, 2017
The performance of the MAX-MIN ant system (MMAS) in dynamic optimization problems (DOPs) is sensi... more The performance of the MAX-MIN ant system (MMAS) in dynamic optimization problems (DOPs) is sensitive to the colony size. In particular, a large colony size may waste computational resources whereas a small colony size may restrict the searching capabilities of the algorithm. There is a trade off in the behaviour of the algorithm between the early and later stages of the optimization process. A smaller colony size leads to better performance on shorter runs whereas a larger colony size leads to better performance on longer runs. In this paper, pre-scheduling of varying the colony size of MMAS is investigated in dynamic environments.
IEEE Transactions on Automation Science and Engineering
Uncertainty is a practical issue in system design optimization because some characteristics of co... more Uncertainty is a practical issue in system design optimization because some characteristics of components, such as reliability and cost, cannot be determined precisely in many situations. Considering the imprecise characteristics of components, few works have focused on the multi-objective optimization for the redundancy allocation due to the challenges of comparing multi intervals. To tackle the issue, a novel angle-based bi-objective redundancy allocation algorithm is proposed in this study, introducing three original contributions: 1) An angle-based interval crowding distance (ICA) is especially designed for effective performance and reduced computational time; 2) Two techniques are applied to tackle the problem: An elite selection for mutation is presented for generating better offsprings; A penalty-guided constraint handling technique is introduced for converting the problem into an unconstrained one. 3) Since a set of optimal solutions is obtained by the proposed method and no preference on uncertainties is provided, this paper proposes a novel knee interval method to help DMs make a decision. To be specific, the proposed ICA can describe the distribution of the whole population intuitively and effectively, considering not only the angle between two compared individuals but also the angle range of the interval values. The computational results from two typical experiments demonstrate that the proposed algorithm is more efficient than other state-of-the-art algorithms, generating Pareto sets with less repeating individuals, stronger convergence, wider distribution, less imprecision, and reduced computational time. Note to Practitioners-This article is motivated by two practical problems in multi-objective redundancy allocation in presence of interval uncertainty: First, this paper tries to solve the multiobjective redundancy allocation problem with the imprecise characteristics of components, which is rarely considered in the field of reliability optimization design. Second, the calculation of the crowding distance needs extra time cost and is less efficient. To tackle this issue, an interval crowding angle is especially designed, considering not only the angle between two compared individuals, but also the angle range of the interval values. The proposed method can be embedded in most multi-objective
Swarm and Evolutionary Computation
In the domain of evolutionary computation, more and more attention has been paid to dynamic multi... more In the domain of evolutionary computation, more and more attention has been paid to dynamic multiobjective optimization. Generally, artificial benchmarks are effective tools for the performance evaluation of dynamic multiobjective evolutionary algorithms (DMOEAs). After reviewing existing benchmarks and highlighting their weaknesses, this paper proposes a new benchmark suite to promote the comprehensive testing of algorithms. This proposed benchmark suite has eight random instances in which the randomness is produced by designed random time sequences. Also, this suite introduces challenging but rarely considered characteristics, including diverse features in fitness landscape (e.g. deception, multimodality, and bias) and complex trade-off geometries (e.g. convexity-concavity mixed geometry and disconnected geometry). Empirical studies have shown that the proposed benchmark poses reasonable challenges to DMOEAs in terms of convergence and diversity. Besides, a center matching $ Fully documented templates are available in the elsarticle package on CTAN.
Information Sciences
This paper proposes a new decison variable classification-based cooperative coevolutionary algori... more This paper proposes a new decison variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic multiobjective problems. In particular, the decision variables are divided into two groups: convergence variables and diversity variables, and different strategies are introduced to optimize these groups. Two kinds of subpopulations are used in the proposed algorithm, i.e., the subpopulations that represent diversity variables (DS) and the subpopulations that represent convergence variables (CS). In the evolution process, the coevolution of DS and CS is carried out through the genetic operators, and subpopulations of CS are gradually merged into DS, which is optimized in the global search space, based on an indicator to avoid becoming trapped in local optimum. Once a change is detected, a prediction method and a diversity introduction approach are adopted for these two kinds of variables to get a promising population with good diversity and convergence in the new environment. The proposed algorithm is tested on 16 benchmark DMOPs
IEEE Transactions on Evolutionary Computation, 2022
The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has attained excellent pe... more The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has attained excellent performance in solving optimization problems involving multiple conflicting objectives. However, the Pareto optimal front (POF) of many multi-objective optimization problems (MOPs) has irregular properties, which weakens the performance of MOEA/D. To address this issue, we devise a dynamic transfer reference point oriented MOEA/D with local objective-space knowledge (DTR-MOEA/D). The design principle is based on three original and rigorous mechanisms. First, the individuals are projected onto a line segment (two-objective case) or a three-dimensional plane (three-objective case) after being normalized in the objective space. The line segment or the plane is divided into three different regions: the central region, the middle region, and the edge region. Second, a dynamic transfer criterion of reference point is developed based on population density relationships in different regions. Third, a strategy of population diversity enhancement guided by local objective-space knowledge is adopted to improve the diversity of the population. Finally, the experimental results conducted on sixteen benchmark MOPs and eight modified MOPs with irregular POF shapes verify that the proposed DTR-MOEA/D has attained a strong competitiveness compared with other representative algorithms.
Proceedings of the 2020 12th International Conference on Machine Learning and Computing, 2020
The study of the vehicle routing problem (VRP) is of outstanding significance for reducing logist... more The study of the vehicle routing problem (VRP) is of outstanding significance for reducing logistics costs. Currently, there is little VRP considering real-time traffic conditions. In this paper, we propose a more realistic and challenging multi-objective VRP containing real-time traffic conditions. Besides, we also offer an adaptive local search algorithm combined with a dynamic constrained multi-objective evolutionary framework. In the algorithm, we design eight local search operators and select them adaptively to optimize the initial solutions. Experimental results show that our algorithm can obtain an excellent solution that satisfies the constraints of the vehicle routing problem with realtime traffic conditions.
Swarm and Evolutionary Computation, 2021
Dynamic multimodal optimization problems (DMMOPs) are a class of problems consisting of two chara... more Dynamic multimodal optimization problems (DMMOPs) are a class of problems consisting of two characteristics, i.e., dynamic and multimodal natures. The former characteristic reveals that the properties of DMMOPs change over time, which is derived from dynamic optimization problems (DOPs). The latter one indicates that there exist multiple global or acceptable local optima, which comes from the multimodal optimization problems (MMOPs). Although there has been much attention to both DOPs and MMOPs in the field of metaheuristics, there is little work devoting to the connection between the dynamic and multimodal natures in DMMOPs. To solve DMMOPs, the strategies dealing with dynamic and multimodal natures in the algorithms should cooperate with each other. Before looking deeply into the connections between two natures, there is necessary to measure the performances of the methods dealing with two natures in DMMOPs. In this paper, first, considering the dynamic and multimodal natures of DMMOPs, we design a set of benchmark problems to simulate various dynamic and multimodal environments. Then, we propose
IEEE Transactions on Cybernetics, 2021
Patient stratification has been studied widely to tackle subtype diagnosis problems for effective... more Patient stratification has been studied widely to tackle subtype diagnosis problems for effective treatment. Due to the dimensionality curse and poor interpretability of data, there is always a long-lasting challenge in constructing a stratification model with high diagnostic ability and good generalization. To address these problems, this paper proposes two novel evolutionary multiobjective clustering algorithms with ensemble (NSGA-II-ECFE and MOEA/D-ECFE) with four cluster validity indices used as the objective functions. First, an effective ensemble construction method is developed to enrich the ensemble diversity. After that, an ensemble clustering fitness evaluation (ECFE) method is proposed to evaluate the ensembles by measuring the consensus clustering under those four objective functions. To generate the consensus clustering, ECFE exploits the hybrid co-association matrix from the ensembles and then dynamically selects the suitable clustering algorithm on that matrix. Multiple experiments have been conducted to demonstrate the effectiveness of the proposed algorithm in comparison with seven clustering algorithms, twelve ensemble clustering approaches, and two multiobjective clustering algorithms on 55 synthetic datasets and 35 real patient stratification datasets. The experimental results demonstrate the competitive edges of the proposed algorithms over those compared methods. Furthermore, the proposed algorithm is applied to extend its advantages by identifying cancer subtypes from five cancer-related single-cell RNA-seq datasets.
Expert Systems with Applications, 2021
Prediction methods are widely used to solve dynamic multi-objective optimization problems (DMOPs)... more Prediction methods are widely used to solve dynamic multi-objective optimization problems (DMOPs). The key to the success of prediction methods lies in the accurate tracking of the new location of the Pareto set (PS) or Pareto front (PF) in a new environment. To improve the prediction accuracy, this paper proposes a novel feedback-based prediction strategy (FPS), which consists of two feedback mechanisms, namely correction feedback (CF) and effectiveness feedback (EF). CF is used to correct an initial prediction model. When the environment changes, CF constructs a representative individual to reflect the characteristics of the current population. The predicted solution of this individual in the new environment is calculated based on the initial prediction model. Afterward, a step size exploration method based on variable classification is introduced to adaptively correct the prediction model. EF is applied to enhance the effectiveness of re-initialization in two stages. In the first stage, half of the individuals in the population are re-initialized based on the corrected prediction model. In the second stage, EF re-initializes the rest of the individuals in the population using two rounds of roulette method based on the re-initialization effectiveness feedback of the first stage. The proposed FPS is incorporated into a dynamic multi-objective optimization evolutionary algorithm (DMOEA) based on decomposition resulting in a new algorithm denoted as MOEA/D-FPS. MOEA/D-FPS is compared with six state-of-the-art DMOEAs on twenty-two different benchmark problems. The experimental results demonstrate the effectiveness and efficacy of MOEA/D-FPS in solving DMOPs.
Swarm and Evolutionary Computation, 2021
Convergence and diversity are two performance requirements that should be paid attention to in ev... more Convergence and diversity are two performance requirements that should be paid attention to in evolutionary algorithms. Most multiobjective evolutionary algorithms (MOEAs) try their best to maintain a balance between the two aspects, which poses a challenge to the convergence of MOEAs in the early evolutionary process. In this paper, a many-objective optimization algorithm based on staged coordination selection, which consists of the convergence and diversity stages, is proposed in which the two stages are considered separately in each iteration. In the convergence exploring stage, the decomposition method is adopted to rapidly make the population close to the true PF. In the diversity exploring stage, a diversity maintenance mechanism same to the archive truncation method of SPEA2 is used to push distributed individuals to the true PF. The convergence stage serves for the diversity stage, and the second stage turns into the first stage when it fails to reach the convergence requirement and so forth. Our algorithm is compared with eight state-of-the-art many-objective optimization algorithms on DTLZ, WFG and MaOP benchmark instances. Results show that our algorithm outperformed
Swarm and Evolutionary Computation, 2021
This paper presents a novel population prediction algorithm based on modular neural network (PA-M... more This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization.
Applied Soft Computing, 2020
While traditional Pareto-based evolutionary multi-objective optimization (E-MO) algorithms have s... more While traditional Pareto-based evolutionary multi-objective optimization (E-MO) algorithms have shown an excellent balance between convergence and diversity on a wide range of practical problems with two or three objectives in real applications, the decision maker (DM) is interested in a unique set of solutions rather than the the whole population on Pareto optimal front (POF). In addition, Pareto-based EMO algorithms have some shortcomings in dealing with many-objective problems because of insuffcient selection pressure toward trade-off solutions. Due to the above, it is crucial to incorporate DM preference information into EMO and seek a representative subset of Pareto optimal solutions with an increase in the number of objectives. This paper proposes a new dominance relationship, called Ra-dominance, which can improve diversity among the Pareto-equivalent solutions increase the selection presure in evolutionary process. It has the ability to guide the population toward areas more responsive to the needs of the DM according to a reference point and prefer-$ Fully documented templates are available in the elsarticle package on CTAN.
Information Sciences, 2020
Population-based evolutionary algorithms have great potential to handle multiobjective optimisati... more Population-based evolutionary algorithms have great potential to handle multiobjective optimisation problems. However, these algorithms depends largely on problem characteristics, and there is a need to improve their performance for a wider range of problems. References, which are often specified by the decision maker's preference in different forms, are a very effective method to improve the performance of algorithms but have not been fully explored in literature. This paper proposes a novel framework for effective use of references to strengthen algorithms. This framework considers references as search targets which can be adjusted based on the information collected during the search. The proposed framework is combined with new strategies, such as reference adaptation and adaptive local mating, to solve different types of problems. The proposed algorithm is compared with state of the arts on a wide range of problems with diverse characteristics. The comparison and extensive sensitivity analysis demonstrate that the proposed algorithm is competitive and robust across different types of problems studied in this paper.
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Papers by Shengxiang Yang