Multi-population Cooperative Particle Swarm Optimization

Niu, Ben; Zhu, Yunlong; He, Xing

doi:10.1007/11553090_88

Cited by 53 publications

(33 citation statements)

References 13 publications

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“…However, the above approaches still have a high probability of premature convergence due to their unsophisticated search strategy. To overcome this difficulty, Niu et al [6] proposed multi-population cooperative PSO (MCPSO), in which the particle swarms consist of one master swarm and several slave swarms. MCPSO uses different search strategies in the master swarm and slave swarms.…”

Section: Pso With Multiswarm Strategymentioning

confidence: 99%

“…Their simulation results proved that MCPSO can avoid premature convergence and produce much better performance than conventional approaches. The multiswarm approach with various search strategies can efficiently prevent the particle swarm from becoming trapped at a local optimal solution, even if the search space becomes more complex or larger [3], [4], [6]- [13].…”

Section: Pso With Multiswarm Strategymentioning

confidence: 99%

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A Fundamental Study of Ultrahigh-Speed Hardware Architecture for Particle Swarm Optimization

Baba

Chen

2018

Journal of Signal Processing

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Particle swarm optimization (PSO) is a powerful method for dealing with the optimization of nonlinear applications such as network optimization, power system optimization and so on. The PSO algorithms enable a higher signal processing speed than conventional approaches and dedicated digital signal processor (DSP) in various application fields. However, the software implementation of PSO is extremely time-consuming when the numbers of iterations and particles become large. In some applications such as smart engine control, nuclear reactor control and so on, real-time control on the order of μm-ms is required, which is extremely difficult to achieve in an embedded system. To realize ultrahigh-speed signal processing of the aforementioned applications, several possible approaches of PSO hardware implementation are discussed and investigated in this paper. A novel hardware architecture for the PSO algorithm is employed to overcome the above difficulties. In this research, the proposed hardware architecture is implemented into a field-programmable gate array (FPGA) to evaluate its performance. The proposed hardware architecture employs two features, which are a serial architecture with two-level pipelines and an adaptive generic particle calculation block (AGPCB). It can not only achieve much faster performance than the conventional approaches but also minimize the chip cost simultaneously. The related research achievements prove that the proposed hardware architecture can greatly reduce the calculation time while achieving high efficiency. In addition, the proposed approach has compatibility with various PSO algorithms to meet the requirements of different nonlinear applications.

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Section: Pso With Multiswarm Strategymentioning

confidence: 99%

Section: Pso With Multiswarm Strategymentioning

confidence: 99%

A Fundamental Study of Ultrahigh-Speed Hardware Architecture for Particle Swarm Optimization

Baba

Chen

2018

Journal of Signal Processing

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“…The CPSO algorithm performs well in the early iterations (i.e., quickly converging towards an optimum in the first period of iterations), while has problems reaching a near optimal solution in some function optimization problems. Various attempts have been made to improve the performance of CPSO, which can be found in literatures [26][27][28][29][30][31][32][33].…”

Section: Canonical Pso Modelmentioning

confidence: 99%

“…The approach introduced in [17,18] relies on having two or more swarms searching concurrently for a solution with frequent interacting, while the interaction frequency is arbitrarily predefined. A multiswarm cooperative particle swarm optimizer (MCPSO) is described in [24,33]. In MCPSO, the population consists of one master swarm and several slave swarms.…”

Section: Introductionmentioning

confidence: 99%

Optimization based on symbiotic multi-species coevolution

Chen

Zhu

2008

Applied Mathematics and Computation

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“…Wang and Li [23] integrate PSO and simulated annealing to improve the performance of PSO. Inspired by the phenomenon of symbiosis in natural ecosystem, Niu et al [24] incorporate master-slave mode into PSO and presents a multi-population cooperative particle swarm optimization (MCPSO). Shelokar et al [25] propose an improved particle swarm optimization hybridized with an ant colony approach, called PSACO (particle swarm ant colony optimization), for optimization of multi-modal continuous functions.…”

Section: Introductionmentioning

confidence: 99%

Improved particle swarm algorithm for hydrological parameter optimization

Jiang

Liu

Huang

et al. 2010

Applied Mathematics and Computation

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a b s t r a c tIn this paper, a new method named MSSE-PSO (master-slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles' position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several subswarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method.

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Multi-population Cooperative Particle Swarm Optimization

Cited by 53 publications

References 13 publications

A Fundamental Study of Ultrahigh-Speed Hardware Architecture for Particle Swarm Optimization

A Fundamental Study of Ultrahigh-Speed Hardware Architecture for Particle Swarm Optimization

Optimization based on symbiotic multi-species coevolution

Improved particle swarm algorithm for hydrological parameter optimization

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