Browsing by Author "Li, Changhe"

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    A reinforcement learning based dynamic multi-objective constrained evolutionary algorithm for open-pit mine truck scheduling
    (IEEE, 2023-09) Qiu, Junxiang; Li, Changhe; Yang, Shengxiang
    Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods.
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    An adaptive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions
    (IEEE Press, 2021-12-05) Chen, Baojian; Li, Changhe; Zeng, Sanyou; Yang, Shengxiang; Mavrovouniotis, Michalis
    The research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multi-objective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms.
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    An adaptive learning particle swarm optimizer for function optimization.
    (IEEE, 2009) Li, Changhe; Yang, Shengxiang
    Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each particle is separated into three parts: its own historical best position, the closest neighbor and the global best one. By using this individual level adaptive technique, a particle can well guide its behavior of exploration and exploitation. A set of 21 test functions were used including un-rotated, rotated and composition functions to test the performance of ALPSO. From the comparison results over several variant PSO algorithms, ALPSO shows an outstanding performance on most test functions, especially the fast convergence characteristic.
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    Adaptive learning particle swarm optimizer-II for global optimization.
    (IEEE., 2010) Li, Changhe; Yang, Shengxiang
    This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO), we call it ALPSO-II. In order to improve the performance of ALPSO on multi-modal problems, we introduce several new major features in ALPSO-II: (i) Adding particle's status monitoring mechanism, (ii) controlling the number of particles that learn from the global best position, and (iii) updating two of the four learning operators used in ALPSO. To test the performance of ALPSO-II, we choose a set of 27 test problems, including un-rotated, shifted, rotated, rotated shifted, and composition functions in comparison of the ALPSO algorithm as well as several state-of-the-art variant PSO algorithms. The experimental results show that ALPSO-II has a great improvement of the ALPSO algorithm, it also outperforms the other peer algorithms on most test problems in terms of both the convergence speed and solution accuracy.
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    An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems
    (American Society of Civil Engineers, 2021-02) Li, Changhe; Yang, Rui; Zhou, Li; Zeng, Sanyou; Mavrovouniotis, Michalis; Yang, Ming; Yang, Shengxiang; Wu, Min
    Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, non-uniqueness, and large scale characteristics. To address the real-time and non-uniqueness challenges, we propose an adaptive multi-population evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a different global optimum. The algorithm adaptively adjusts the number of populations using a feed-back learning mechanism. To effectively locate an optimal solution for a population, a co-evolutionary strategy is used to identify the location and the injection profile separately. Experimental results on three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms.
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    An Adaptive Multi-Population Framework for Locating and Tracking Multiple Optima
    (IEEE Press, 2015-11-30) Li, Changhe; Nguyen, T. T.; Ming, Yang; Mavrovouniotis, Michalis; Yang, Shengxiang
    Multi-population methods are effective to solve dynamic optimization problems. However, to efficiently track multiple optima, algorithm designers need to address a key issue: how to adapt the number of populations. In this paper, an adaptive multi-population framework is proposed to address this issue. A database is designed to collect heuristic information of algorithm behavior changes. The number of populations is adjusted according to statistical information related to the current evolving status in the database and a heuristic value. Several other techniques are also introduced, including a heuristic clustering method, a population exclusion scheme, a population hibernation scheme, two movement schemes, and a peak hiding method. The particle swarm optimization and differential evolution algorithms are implemented into the framework, respectively. A set of multi-population based algorithms are chosen to compare with the proposed algorithms on the moving peaks benchmark using four different performance measures. The effect of the components of the framework is also investigated based on a set of multi-modal problems in static environments. Experimental results show that the proposed algorithms outperform the other algorithms in most scenarios.
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    An adaptive multi-swarm optimizer for dynamic optimization problems
    (The MIT Press, 2014-01-17) Li, Changhe; Yang, Shengxiang; Yang, Ming
    The multi-population method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take the challenging issues into account to design such adaptive algorithms. To address the issues when multi-population methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multi-population methods from different research areas in the literature of evolutionary computation.
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    An adaptive mutation operator for particle swarm optimization.
    (2008) Li, Changhe; Yang, Shengxiang; Korejo, Imtiaz
    Particle swarm optimization (PSO) is an e cient tool for optimization and search problems. However, it is easy to be trapped into local optima due to its information sharing mechanism. Many research works have shown that mutation operators can help PSO prevent premature convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compared for PSO. An adaptive mutation operator is designed. Experimental results show that these mutation operators can greatly enhance the performanceof PSO. The adaptive mutation operator shows great advantages over non-adaptive mutation operators on a set of benchmark test problems.
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    Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem
    (IEEE Press, 2019-10) Mavrovouniotis, Michalis; Yang, Shengxiang; Van, Mien; Li, Changhe; Marios, Polycarpou
    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.
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    Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments
    (2022-01) Luo, Wenjian; Lin, Xin; Li, Changhe; Yang, Shengxiang; Shi, Yuhui
    Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolu tionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.
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    Benchmark generator for CEC 2009 competition on dynamic optimization
    (University of Leicester, U.K., 2008-10) Li, Changhe; Yang, Shengxiang; Nguyen, T. T.; Ling Yu, E.; Yao, Xin; Jin, Yaochu; Beyer, H. -G.; Suganthan, P. N.
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    Benchmark Generator for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems
    (Brunel University, U.K., 2011-10) Li, Changhe; Yang, Shengxiang; Pelta, David A.
    Based on our previous benchmark generator for the IEEE CEC’09 Competition on Dynamic Optimization, this report updates the two benchmark instances where a new change type has been developed as well as a constraint to the benchmark instance of the dynamic rotation peak benchmark generator.
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    Benchmark generator for the IEEE WCCI-2012 competition on evolutionary computation for dynamic optimization problems. Technical Report 2011.
    (Department of Information Systems and Computing, Brunel University., 2011) Li, Changhe; Yang, Shengxiang; Pelta, David A.
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    Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Rotation Peak Benchmark Generator (DRPBG) and Dynamic Composition Benchmark Generator (DCBG)
    (De Montfort University, UK, 2013-10) Li, Changhe; Mavrovouniotis, Michalis; Yang, Shengxiang; Yao, Xin
    Based on our previous benchmark generator for the IEEE CEC’12 Competition on Dynamic Optimization, this report updates the two benchmark instances where two new features have 1been developed as well as a constraint to the benchmark instance of the dynamic rotation peak benchmark generator. The source code in C++ language for the two benchmark instances is included in the library of EAlib, which is an open platform to test and compare the performances of EAs.
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    Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Travelling Salesman Problem Benchmark Generator
    (De Montfort University, U.K., 2013-10) Mavrovouniotis, Michalis; Li, Changhe; Yang, Shengxiang; Yao, Xin
    In this report, the dynamic benchmark generator for permutation-encoded problems for the travelling salesman problem (DBGPTSP) proposed in is used to convert any static travelling salesman problem benchmark to a dynamic optimization problem, by modifying the encoding of the instance instead of the fitness landscape.
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    Bridging the gap between theory and practice: Fitness landscape analysis of real-world problems with nearest-better network
    (MDPI, 2025-03) Diao, Yiya; Li, Changhe; Wang, Junchen; Zeng, Sanyou; Yang, Shengxiang
    For a long time, there has been a gap between theoretical optimization research and real-world applications. A key challenge is that many real-world problems are blackbox problems, making it difficult to identify their characteristics and, consequently, select the most effective algorithms to solve them. Fortunately, the Nearest-Better Network has emerged as an effective tool for analyzing the characteristics of problems, regardless of dimensionality. In this paper, we conduct an in-depth experimental analysis of real-world functions from the CEC 2022 and CEC 2011 competitions using the NBN. Our experiments reveal that real-world problems often exhibit characteristics such as unclear global structure, multiple attraction basins, vast neutral regions around the global optimum, and high levels of ill conditioning.
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    A clustering particle swarm optimizer for dynamic optimization.
    (IEEE, 2009) Li, Changhe; Yang, Shengxiang
    In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.
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    A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments
    (IEEE, 2010) Yang, Shengxiang; Li, Changhe
    In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.
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    A comparative study of adaptive mutation operators for metaheuristics.
    (2009) Kojero, Imtiaz; Yang, Shengxiang; Li, Changhe
    Genetic algorithms (GAs) are a class of stochastic optimization methods inspired by the principles of natural evolution. Adaptation of strategy parameters and genetic operators has become an important and promising research area in GAs. Many researchers are applying adaptive techniques to guide the search of GAs toward optimum solutions. Mutation is a key component of GAs. It is a variation operator to create diversity for GAs. This paper investigates several adaptive mutation operators, including population level adaptive mutation operators and gene level adaptive mutation operators, for GAs and compares their performance based on a set of uni-modal and multi-modal benchmark problems. The experimental results show that the gene level adaptive mutation operators are usually more efficient than the population level adaptive mutation operators for GAs.
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    A comparative study on particle swarm optimization in dynamic environments.
    (Springer-Verlag, 2013) Li, Changhe; Yang, Shengxiang