Key Laboratory of Software Technology

Laboratory of, China

Key Laboratory of Software Technology

Laboratory of, China

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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.


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.


Shen M.,Beijing Information Science and Technology University | Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Software Technology | Chen W.-N.,Sun Yat Sen University | And 6 more authors.
IEEE Transactions on Industrial Electronics | Year: 2014

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the nondeterministic polynomial (NP) complete multicast routing problem (MRP). The main contribution is the extension of particle swarm optimization (PSO) from the continuous domain to the binary or discrete domain. First, a novel bi-velocity strategy is developed to represent the possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP, where 1 stands for a node being selected to construct the multicast tree, whereas 0 stands for being otherwise. Second, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in the continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the Operation Research Library (OR-library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly since it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on genetic algorithms, ant colony optimization, and PSO. © 2014 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.


Yu W.-J.,Sun Yat Sen University | Yu W.-J.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | Zhang J.,Key Laboratory of Software Technology
Genetic and Evolutionary Computation Conference, GECCO'11 | Year: 2011

Differential evolution (DE) is one of the most successful evolutionary algorithms (EAs) for global numerical optimization. Like other EAs, maintaining population diversity is important for DE to escape from local optima and locate a near-global optimum. Using a multi-population algorithm is a representative method to avoid early loss of population diversity. In this paper, we propose a multi-population DE algorithm (MPDE) which manipulates multiple sub-populations. Different sub-populations in MPDE exchange information via a novel mutation operation instead of migration used in most multi-population EAs. The mutation operation is helpful to balance the fast convergence and population diversity of the proposed algorithm. Moreover, the performance of MPDE is further improved by an adaptive parameter control scheme designed based on the multi-population approach. Each sub-population in MPDE evolves with its own set of control parameters, and a learning strategy is used to adaptively adjust the parameter values. A set of benchmark functions is used to test the proposed MPDE algorithm. The experimental results show that MPDE performs better than, or at least comparably, to the classical single population DE with fixed parameter values and three existing state-of-the-art DE variants. Copyright 2011 ACM.


Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Laboratory of Digital Life | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 4 more authors.
IEEE Transactions on Evolutionary Computation | Year: 2011

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood's best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSO-L algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness. © 2006 IEEE.


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).


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.


Li Y.-L.,Key Laboratory of Machine Intelligence and Advanced Computing | Li Y.-L.,Key Laboratory of Software Technology | Zhou Y.-R.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhou Y.-R.,Key Laboratory of Software Technology | And 4 more authors.
IEEE Transactions on Evolutionary Computation | Year: 2016

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have been studied a lot and have been widely and successfully used in practice. However, there are no related theoretical studies on this kind of MOEAs. In this paper, we theoretically analyze the MOEAs based on decomposition. First, we analyze the runtime complexity with two basic simple instances. In both cases the Pareto front have one-to-one map to the decomposed subproblems or not. Second, we analyze the runtime complexity on two difficult instances with bad neighborhood relations in fitness space or decision space. Our studies show that: 1) in certain cases, polynomial-sized evenly distributed weight parameters-based decomposition cannot map each point in a polynomial sized Pareto front to a subproblem; 2) an ideal serialized algorithm can be very efficient on some simple instances; 3) the standard MOEA based on decomposition can benefit a runtime cut of a constant fraction from its neighborhood coevolution scheme; and 4) the standard MOEA based on decomposition performs well on difficult instances because both the Pareto domination-based and the scalar subproblem-based search schemes are combined in a proper way. © 1997-2012 IEEE.


Zhan Z.-H.,University of South Africa | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,University of South Africa | Zhang J.,Key Laboratory of Software Technology | Shi Y.-H.,Xi'an Jiaotong - Liverpool University
3rd International Workshop on Advanced Computational Intelligence, IWACI 2010 | Year: 2010

Particle swarm optimization (PSO) has been witnessed fast developments these years for the algorithm performance improvements and the applications in real-world problems. However, the experimental study on the population diversities is not taken seriously by the PSO researchers. This paper intends to make a comprehensive experimental study on the PSO diversity, in order to monitor the evolutionary process of the PSO algorithms, and also to give some discussions based on the observations of the experimental results.

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