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Zhong J.-H.,Sun Yat Sen University | Zhong J.-H.,Key Laboratory of Digital Life | Zhong J.-H.,Key Laboratory of Software Technology | Shen M.,Beijing Information Science and Technology University | And 6 more authors.
IEEE Transactions on Evolutionary Computation | Year: 2013

Railway timetable scheduling is a fundamental operational problem in the railway industry and has significant influence on the quality of service provided by the transport system. This paper explores the periodic railway timetable scheduling (PRTS) problem, with the objective to minimize the average waiting time of the transfer passengers. Unlike traditional PRTS models that only involve service lines with fixed cycles, this paper presents a more flexible model by allowing the cycle of service lines and the number of transfer passengers to vary with the time period. An enhanced differential evolution (DE) algorithm with dual populations, termed 'dual-population DE' (DP-DE), was developed to solve the PRTS problem, yielding high-quality solutions. In the DP-DE, two populations cooperate during the evolution; the first focuses on global search by adopting parameter settings and operators that help maintain population diversity, while the second one focuses on speeding up convergence by adopting parameter settings and operators that are good for local fine tuning. A novel bidirectional migration operator is proposed to share the search experience between the two populations. The proposed DP-DE has been applied to optimize the timetable of the Guangzhou Metro system in Mainland China and six artificial periodic railway systems. Two conventional deterministic algorithms and seven highly regarded evolutionary algorithms are used for comparison. The comparison results reveal that the performance of DP-PE is very promising. © 1997-2012 IEEE.


Guo Y.,Sun Yat Sen University | Guo Y.,Key Laboratory of Digital Life | Guo Y.,Key Laboratory of Software Technology | Chen Z.-R.,Sun Yat Sen University | And 8 more authors.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics | Year: 2012

Cross-docking is now widely applied to trucking industry, for which the optimal schedule of the trucks is a crucial issue. In the cross-docking scheduling problem, the objectives of minimizing the operation cost and maximizing the possibility of punctuality are both important. In this paper, a non-dominated sorting genetic algorithm version II (NSGA-II) with a novel greedy local search strategy is proposed to solve the multi-objective optimization problem. NSGA-II can provide decision makers with flexible choices among the different trade-off solutions, while the local-search strategy is employed to accelerate the convergence speed. In the experiments, four criteria are applied to evaluate the strengths of the proposed algorithm. Experimental results on both small and large size of problems show the accuracy and efficiency of the propose strategy. © 2012 IEEE.


Lai X.,South China University of Technology | Lai X.,Shangrao Normal University | Zhou Y.,South China University of Technology | He J.,Aberystwyth University | And 3 more authors.
IEEE Transactions on Evolutionary Computation | Year: 2014

A few experimental investigations have shown that evolutionary algorithms (EAs) are efficient for the minimum label spanning tree (MLST) problem. However, we know little about that in theory. In this paper, we theoretically analyze the performances of the (1+1) EA, a simple version of EA, and a simple multiobjective evolutionary algorithm called GSEMO on the MLST problem. We reveal that for the MLSTbproblem, the (1+1) EA and GSEMO achieve a b+1/2-approximation ratio in expected polynomial runtime with respect to n , the number of nodes, and k , the number of labels. We also find that GSEMO achieves a (2ln n+1) -approximation ratio for the MLST problem in expected polynomial runtime with respect to n and k. At the same time, we show that the (1+1) EA and GSEMO outperform local search algorithms on three instances of the MLST problem. We also construct an instance on which GSEMO outperforms the (1+1) EA. © 1997-2012 IEEE.


Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Digital Life | Zhan Z.-H.,Key Laboratory of Software Technology | Li J.,Hong Kong Polytechnic University | And 6 more authors.
IEEE Transactions on Cybernetics | Year: 2013

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs. © 2012 IEEE.


Zhong J.-H.,Sun Yat Sen University | Zhong J.-H.,Key Laboratory of Digital Life | Zhong J.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 2 more authors.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation | Year: 2012

Differential Evolution is a new paradigm of evolutionary algorithm which has been widely used to solve nonlinear and complex problems. The performance of DE is mainly dependent on the parameter settings, which relate to not only characteristics of the specific problem but also the evolution state of the algorithm. Hence, determining the suitable parameter settings of DE is a promising but challenging task. This paper presents an enhanced algorithm, namely, the stochastic coding differential evolution, to improve the robustness and efficiency of DE. Instead of encoding each individual as a vector of floating point numbers, the proposed SDE represents each individual by a multivariate normal distribution. In this way, individuals in the population can be more sensible to their surrounding regions and the algorithm can explore the search space region-by-region. In the SDE, a newly designed update operator and a random mutation operator are incorporated to improve the algorithm performance. Traditional DE operators such as the mutation scheme and the crossover operator are also accordingly extended. The proposed SDE has been validated by nine benchmark test functions with different characteristics. Five EAs are compared in the experiment study. The comparison results demonstrate the effectiveness and efficiency of the SDE. © 2012 ACM.


Li Y.-L.,Sun Yat Sen University | Chen W.-N.,Key Laboratory of Digital Life | Zhang J.,Key Laboratory of Software Technology
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion | Year: 2012

Crossover is a very important operation in current differential evolution (DE) algorithms. The existing crossover strategies in DE show promising effects especially when the algorithms are applied to separable functions. However, the operation fails to work well when applied to the ill-conditioned and inseparable problems because the recombination of good genes is no longer promising for generating better individuals. In this paper, we propose to use the principal component analysis (PCA) technique to rebuild a coordinate system. With this system, the correlations among variables are decreased for the crossover operation of DE and the crossover operation become more efficient. Copyright is held by the author/owner(s).


Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Digital Life | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 2 more authors.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion | Year: 2012

This paper proposes a novel differential evolution (DE) algorithm with random walk (DE-RW). Random walk is a famous phenomenon universally exists in nature and society. As random walk is an erratic movement that can go in any direction and go to any place, it is likely that this mechanism can be used in search algorithm to bring in diversity. We apply the random walk mechanism into conventional DE variants with different parameters. Experiments are conducted on a set of benchmark functions with different characteristics to demonstrate the advantages of random walk in avoiding local optima. Experimental results show that DE-RWs have general better performance than their corresponding conventional DE variants, especially on multimodal functions. Copyright is held by the author/owner(s).


Chen N.,Sun Yat Sen University | Chen N.,Key Laboratory of Digital Life | Chen N.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 3 more authors.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion | Year: 2012

The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method modifies the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals. Copyright is held by the author/owner(s).


Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Digital Life | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 4 more authors.
Proceedings - 2012 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2012 | Year: 2012

This paper proposes to use the binary particle swarm optimization (BPSO) approach to solve the disjoint set covers (DSC) problem in the wireless sensor networks (WSN). The DSC problem is to divide the sensor nodes into different disjoint sets and schedule them to work one by one in order to save energy while at the same time meets the surveillance requirement, e.g., the full coverage. The objective of DSC is to maximal the number of disjoint sets. As different disjoint sets form and work successively, only the sensors from the current set are responsible for monitoring the area, while nodes from other sets are sleeping to save energy. Therefore the DSC is a fundamental problem in the WSN and is significant for the network lifetime. In the literature, BPSO has been successfully applied to solve the optimal coverage problem (OCP) which is to find a subset of sensors with the minimal number of sensors to fully monitor the area. In this paper, we extend the BPSO approach to solve the DSC problem by solving the OCP again and again to find the disjoint subsets as many as possible. Once finding the minimal number of sensors for the OCP to fully monitor the area, we mark these sensors as unavailable and repeatedly find another subset of sensors in the remained WSN for the OCP. This way, BPSO can find disjoint subsets of the WSN as many as possible, which is the solution to the DSC problem. Simulations have been conducted to evaluate the performance of the proposed BPSO approach. The experimental results show that BPSO has very good performance in maximizing the disjoint sets number when compared with the traditional heuristic and the genetic algorithm approaches. © 2012 IEEE.


Zhong J.-H.,Sun Yat Sen University | Zhong J.-H.,Key Laboratory of Digital Life | Zhong J.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 2 more authors.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation | Year: 2012

In wireless sensor networks (WSNs), sensors near the sink can be burdened with a large amount of traffic, because they have to transmit data generated by themselves and those far away from the sink. Hence the sensors near the sink would deplete their energy much faster than the others, which results in a short network lifetime. Using mobile sink is an effective way to tackle this issue. This paper explores the problem of determining the optimal movements of the mobile sink to maximize the network lifetime. A novel ant colony optimization algorithm (ACO), namely the ACO-MSS, is developed to solve the problem. The proposed ACO-MSS takes advantage of the global search ability of ACO and adopts effective heuristic information to find a near globally optimal solution. Multiple practical factors such as the forbidden regions and the maximum moving distance of the sink are taken into account to facilitate the real applications. The proposed ACO-MSS is validated by a series of simulations on WSNs with different characteristics. The simulation results demonstrate the effectiveness of the proposed algorithms. © 2012 ACM.

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