Lei Wang

Tongji University, Shanghai, Shanghai Shi, China

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Publications (49)7.12 Total impact

  • 05/2014; 11(6).
  • Weian Guo, Lei Wang, Qidi Wu
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    ABSTRACT: Biogeography-Based Optimization (BBO), inspired by the science of biogeography, is a novel population-based Evolutionary Algorithm (EA). For optimization problems, BBO builds the matching mathematical model of the organism distribution. In this evolutionary mechanism, species migrating among islands can be considered as the information transition among different solutions represented by habitats. Solutions are reassembled according to migration rates. However, so far, the migration models are generally designed by empirical studies. This leads to immature conclusions that are unreliable. To complete the previous works, this paper investigates transition probability matrices of BBO to clarify that the transition probability of median number of species is not the only determinant factor to influence performance. The impact of migration rates on BBO is mathematically discussed, which is helpful to design migration models. Using numerical simulations, the BBO and several other classical evolutionary algorithms are compared. The simulations also comprehensively explain the effect of the BBO's properties on its performance including dimension, population size, and migration models. The results validate the theoretical analysis in this paper.
    Information Sciences: an International Journal. 01/2014; 254:111-140.
  • Soft Computing, to appear. 01/2014;
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    ABSTRACT: The tolerance value plays an important role when converting equality constraints into inequality constraints in solving Constrained Optimization Problems. Many researchers use a fixed or dynamic setting directly based on trial or experiments without systematic study. As a well-known constraint handling technique, Deb's feasibility-based rule is widely adopted, but it has one drawback as the ranking is not consistent with the actual ranking after introducing the tolerance value. After carefully analyzing how the tolerance value influences the ranking difference, a novel strategy named Ranking Adjustment Strategy (RAS) is proposed, which can be considered as a complement of Deb's feasibility-based rule. The experiment has verified the effectiveness of the proposed strategy. This is the first time to analyze the inner mechanism of the tolerance value for equality constraints systematically, which can give some guide for future research.
    Theoretical Computer Science. 01/2014;
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    ABSTRACT: Bat algorithm is a novel branch of evolutionary computation. Although there are several research papers that focus on this new algorithm, however, few of them concerns the high-dimensional numerical problems. In this paper, a new variant called bat algorithm with Gaussian walk (BAGW) is proposed aiming to solve this problem. In this variant, a Gaussian walk is employed in the local turbulence instead of the original uniform walk to improve the local search capability. Furthermore, to keep the high exploitation pressure, the velocity update equation is also changed. Finally, to increase the population diversity, the frequency is dominated by each dimension in our modification, as well as it is depended on the different bat in the standard version. To test the performance of our variant, four famous un-constraint numerical benchmarks are employed, and test on different dimensional cases, simulation results show our modification is effective.
    Int. J. of Bio-Inspired Computation. 01/2014; 6(3):166 - 174.
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    ABSTRACT: As increasing numbers of electric vehicles (EVs) enter into the society, the charging behavior of EVs has got lots of attention due to its economical difference within the electricity market. The charging cost for EVs generally differ from each other in choosing the charging time interval (hourly), since the hourly electricity prices are different in the market. In this paper, the problem is formulated into an optimal control one and solved by dynamic programming. Optimization aims to find the economically optimal charging solution for each vehicle. In this paper, a nonlinear battery model is characterized and presented, and a given future electricity prices is assumed and utilized. Simulation results indicate that daily charing cost is reduced by smart charing.
    Neural Computing and Applications 12/2013; · 1.76 Impact Factor
  • Journal of Computational and Theoretical Nanoscience, vol. 10, issue 6, pp. 1545-1549. 06/2013; 10(6):1545-1549.
  • Journal of Computational and Theoretical Nanoscience, vol. 10, issue 6, pp. 1550-1554. 06/2013; 10(6):1550-1554.
  • Source
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    ABSTRACT: As a novel heuristic optimisation algorithm, biogeography-based optimisation (BBO) has a huge potential to be further developed. Genetic algorithm (GA) is a famous algorithm in optimisation as well. In this paper, two hybrid algorithms of BBO and GA are proposed based on elites operations. According to the property of the two algorithms, we optimised the elites' migration model in BBO by using GA. The one is named global migration hybrid strategy (GMHS), and the other is hierarchical migration hybrid strategy (HMHS). From the test results, it is obvious that the two strategies both perform better than BBO or GA alone. In addition, some comparisons among the new two hybrid strategies and other famous hybrid algorithms are shown in this paper. And an application of semiconductor manufacturing lines is implemented by the hybrid algorithm. According to the results, we know the hybrid strategies have a better capability to solve optimisation problems.
    Int. J. of Modelling. 01/2013; 18(1):9 - 17.
  • Jing An, Qi Kang, Lei Wang, Qidi Wu
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    ABSTRACT: Over the last decade, we have encountered various complex optimization problems in the engineering and research domains. Some of them are so hard that we had to turn to heuristic algorithms to obtain approximate optimal solutions. In this paper, we present a novel metaheuristic algorithm called mussels wandering optimization (MWO). MWO is inspired by mussels’ leisurely locomotion behavior when they form bed patterns in their habitat. It is an ecologically inspired optimization algorithm that mathematically formulates a landscape-level evolutionary mechanism of the distribution pattern of mussels through a stochastic decision and Lévy walk. We obtain the optimal shape parameter μ of the movement strategy and demonstrate its convergence performance via eight benchmark functions. The MWO algorithm has competitive performance compared with four existing metaheuristics, providing a new approach for solving complex optimization problems.
    Cognitive Computation 01/2013; 5(2). · 0.87 Impact Factor
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    ABSTRACT: In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.
    Engineering Optimization 01/2013; · 0.96 Impact Factor
  • Yongwei Zhang, Lei Wang, Qidi Wu
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    ABSTRACT: Bio-inspired algorithms, through imitating the regular pattern of life forms, often produce unexpected results. A novel global optimisation algorithm, Cuckoo Search (CS), is an example that simulates the brood behaviour of some species of cuckoos. By using Lévy distribution, the flying pattern of cuckoos is also imitated. However, the potential of cuckoo's search pattern is not fully discovered in CS algorithm. In this article, we introduce the CS algorithm and associated Lévy flights. A Modified Adaptive Cuckoo Search (MACS) is then proposed by introducing grouping, parallel, incentive, adaptive and information-sharing characteristics. Also, the formal descriptions of improving strategies are given. The proposed algorithm improves the basic CS algorithm without losing the characteristic of high-efficiency search of Lévy flights. Experiment results show that MACS outperforms basic CS algorithm on most test problems and possesses application potential for real-world problems.
    International Journal of Computer Applications in Technology 08/2012; 44(2):73-79.
  • Yongwei Zhang, Lei Wang, Qidi Wu
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    ABSTRACT: This paper propose a hybrid stochastic approach called differential annealing algorithm. The algorithm integrated the advantages of differential evolution and simulated annealing. It can be considered as a swarm-based simulated annealing with differential operator or differential evolution with the Boltzmann-type selection operator. The proposed algorithm is tested on benchmark functions, along with simulated annealing and differential evolution. Results show that differential annealing outperforms the comparative group under the same amount of function evaluations.
    Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I; 06/2012
  • Jing An, Qi Kang, Lei Wang, Qidi Wu
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    ABSTRACT: This paper presents a population-based approximate scheduling approach for complex production process, by using heuristic stochastic optimisation strategies. In this approach, particle swarm optimisation (PSO) is adopted to find a near optimal operation sequence and schedule strategy based on the criterion of minimal total make-span (TMS) in its admissible sequence space. Discrete dynamic programming method is integrated for the usage of fitness evaluation. A minifab model is studied to illustrate the proposed population-based scheduling algorithm (PSA), which can approach the optimal results by computing partial solution sequences.
    International Journal of Computer Applications in Technology 06/2012; 43(4):304-310.
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    ABSTRACT: This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.
    Neurocomputing. 01/2012; 78:55-63.
  • Chengyong Si, Lei Wang, QiDi Wu
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    ABSTRACT: During the past few decades, many Evolutionary Algorithms together with the constraint handling techniques have been developed to solve the constrained optimization problems which have attracted a lot of research interest. But it's still very difficult to decide when and how to use these algorithms and constraint handling techniques effectively. Some researchers have proposed some general frameworks like population-based algorithm portfolios (PAP), cooperative coevolving or ensemble strategies which use different subpopulations to run the algorithm parallel. These ideas don't consider the problems' characteristics in detail. Motivated by these observations, we propose a new method to construct the relationship between problems and algorithms as well as the constraint handling techniques standing the qualitative and quantitative point of view. This paper first summaries and extracts the problems' characteristics systematically, then combines different qualitative and quantitative methods in the Evolutionary Algorithms and constraint handling techniques respectively so as to get a reasonable correspondence. The experimental results confirm this relationship, which is valuable to guide future research.
    Evolutionary Computation (CEC), 2012 IEEE Congress on; 01/2012
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    ABSTRACT: As is well known, the semiconductor manufacturing is one of the most complicated manufacturing processes. It can be considered as a Job shop Scheduling Problem(JSP), which is classified NP-complete problem. In this kind of problem, the combination of goals and resources can exponential increase the complexity, because a much larger searching space and more constrains exist among tasks. Ant colony optimization, as an effective meat-heuristic technique, can be adopted to find a optimized solution. In this paper, the scheduling problem of semiconductor manufacturing lines is solved by adopting ant colony optimization. The result shows that ACO performs better than some other well known algorithms and the problem can be well solved by ACO.
    Cognitive Informatics & Cognitive Computing (ICCI*CC), 2012 IEEE 11th International Conference on; 01/2012
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    ABSTRACT: A VSD system, which consists of an inverter & an induction motor, is now widely used in all kinds of application. But from the view point of an end user, neither the motor parameters in the mathematics model nor the vector controller structure are known. In this paper a PSO algorithm is programmed with IEC61131-3 language to estimate the parameters for the VSD system, based on the hardware of a vector controlled inverter, in order to reach the similar dynamic performance as a DC motor system. The PSO algorithm could be a kind of alternative approach of present parameter identification functions, for its requirements on the speed of CPU and volume of memory are low, while it converges quickly. It's especially helpful for the adjustment of complicated control system, when the technical requirements are clear & measurable.
    Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I; 01/2011
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    ABSTRACT: This paper presents an efficient and reliable approach based on swarm intelligence to solve the optimal power flow problem. The optimal setting of distributed generations (DGs) is addressed if one or more generators broken down in a power distribution system, to achieve minimization of fuel cost and voltage profile stability. The proposed approach employs particle swarm optimization (PSO) and group search optimizer (GSO) for optimal setting of DGs. These algorithms are executed and compared on IEEE 14-bus test system, respectively. The results confirm the effectiveness and potential application of the proposed swarm-based optimization method in power distribution systems.
    Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, Alaska, USA, October 9-12, 2011; 01/2011
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    ABSTRACT: Process parameter window selection in semiconductor manufacturing field is usually the problem to find out the ranges of input parameters that meet production requirements, which requires allocating optima of a multimodal function efficiently. To achieve good results under the conditions of multimodal model and process control requirement, a NichePSO algorithm based method for parameter window selection is presented in this paper. Both simulation results and production validation data indicate it is an effective method for process parameter window selection.
    Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I; 01/2011

Publication Stats

64 Citations
7.12 Total Impact Points

Institutions

  • 2002–2014
    • Tongji University
      • • College of Electronics and Information Engineering
      • • Department of Control Science and Engineering
      Shanghai, Shanghai Shi, China
  • 2008
    • Shanghai Institute of Technology
      Shanghai, Shanghai Shi, China