Browsing by Author "Wang, Dingwei"

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    Adaptive primal–dual genetic algorithms in dynamic environments.
    (IEEE, 2009) Wang, Hongfeng; Yang, Shengxiang; Ip, W. H.; Wang, Dingwei
    Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.
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    Agent based evolutionary dynamic optimization.
    (Springer-Verlag., 2010) Yan, Yang; Yang, Shengxiang; Wang, Dazhi; Wang, Dingwei
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    Compound particle swarm optimization in dynamic environments.
    (Springer-Verlag., 2008) Liu, Lili; Wang, Dingwei; Yang, Shengxiang
    Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of particles in the search space and their motions are integrated into the swarm. A special reflection scheme is introduced in order to explore the search space more comprehensively. Furthermore, some information preserving and anti-convergence strategies are also developed to improve the performance of CPSO in a new environment. An experimental study shows the efficiency of CPSO in dynamic environments.
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    Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling
    (Elsevier Science Ltd, 1999) Yang, Shengxiang; Wang, Dingwei
    An efficient constraint satisfaction based adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The adaptive neural network has the property of adaptively adjusting its connection weights and biases of neural units according to the sequence and resource constraints of job-shop scheduling problem while solving feasible solution. Two heuristics are used in the hybrid approach: one is used to accelerate the solving process of neural network and guarantee its convergence, the other is used to obtain non-delay schedule from solved feasible solution by neural solution by neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and excellent efficiency.
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    Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling
    (IEEE Press, 2000-03-01) Wang, Dingwei; Yang, Shengxiang
    This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.
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    Evolutionary algorithms in dynamic environments
    (Northeastern University Press， China, 2007-02) Wang, Hongfeng; Wang, Dingwei; Yang, Shengxiang
    Evolutionary algorithms (EAs) are widely and often used for solving stationary optimization problems where the fitness landscape or objective function does not change during the course of computation. However, the environments of real world optimization problems may fluctuate or change sharply. If the optimization problem is dynamic, the goal is no longer to find the extrema, but to track their progression through the search space as closely as possible. All kinds of approaches that have been proposed to make EAs suitable for the dynamic environments are surveyed, such as increasing diversity, maintaining diversity, memory based approaches, multi-population approaches and so on.
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    Genetic algorithm and adaptive neural network hybrid method for job-shop scheduling problems
    (Northeastern University Press, China, 1998-07) Yang, Shengxiang; Wang, Dingwei
    This paper proposes a hybrid method of genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) for solving job-shop scheduling problems. In the hybrid method GA is used to iterate for searching optimal solutions, CSANN is used to solve feasible solutions during the iteration of GA. Computer simulations have shown the good performance of the proposed hybrid method for job-shop scheduling problems.
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    Genetic algorithm and neural network hybrid approach for job-shop scheduling
    (ACTA Press, 1998) Zhao, Kai; Yang, Shengxiang; Wang, Dingwei
    This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.
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    An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems.
    (Springer-Verlag, 2009) Liu, Lili; Wang, Dingwei; Yang, Shengxiang
    In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.
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    An improved constraint satisfaction adaptive neural network for job-shop scheduling.
    (Springer-Verlag, 2010) Yang, Shengxiang; Wang, Dingwei; Chai, Tianyou; Kendall, Graham
    This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.
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    A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems.
    (Springer-Verlag, 2009) Wang, Hongfeng; Wang, Dingwei; Yang, Shengxiang
    Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.
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    A multi-agent based evolutionary algorithm in non-stationary environments.
    (IEEE., 2008) Yan, Yang; Wang, Hongfeng; Wang, Dingwei; Yang, Shengxiang; Wang, Dazhi
    In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms.
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    A neural network and heuristics hybrid strategy for job-shop scheduling problem
    (1999-06) Yang, Shengxiang; Wang, Dingwei
    A new efficient neural network and heuristics hybrid strategy for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving feasible solution. Heuristics are used to accelerate the solving process of neural network and guarantee its convergence, and to obtain non-schedule schedule from solved feasible solution by neural network with orders of operations determined and unchanged. Computer simulations have shown that the proposed hybrid strategy is of high speed and excellent efficiency.
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    A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
    (Elsevier, 2001-08-09) Yang, Shengxiang; Wang, Dingwei
    A new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.
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    A particle swarm optimization based memetic algorithm for dynamic optimization problems.
    (Springer-Verlag, 2010) Yang, Shengxiang; Wang, Hongfeng; Ip, W. H.; Wang, Dingwei
    Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.
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    Solving optimization and scheduling problems with neural network methods
    (1997-12) Yang, Shengxiang; Wang, Dingwei
    This paper briefly reviewed the applications of neural networks in optimization and scheduling problems. The background of combining neural networks with optimization and scheduling problems is first introduced, and the common problem of optimization and scheduling are described briefly. Following that, this paper gives out various neural network models for optimization and scheduling problems and their comparisons, and shows the main models. Finally the conclusion and future research in this field are proposed briefly.
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    Triggered memory-based swarm optimization in dynamic environments
    (Springer, 2007) Wang, Hongfeng; Wang, Dingwei; Yang, Shengxiang
    In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems.
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    Using constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling
    (1999-04) Yang, Shengxiang; Wang, Dingwei
    This paper proposes a new adaptive neural network , based on constraint satisfaction, and efficient heuristics hybrid algorithm for job-shop scheduling. The neural network has the property of adapting its connection weights and biases of neural units while solving feasible solution. Heuristics are used to improve he property of neural network and to obtain local optimal solution from solved feasible solution by neural network with orders of operations determined and unchanged. Computer simulations have shown that the proposed hybrid algorithm is of high speed and excellent efficiency.