Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization

Zhao, Sen; Liang, Jing; Suganthan, Ponnuthurai Nagaratnam; Taşgetiren, M. Fatih

doi:10.1109/cec.2008.4631320

Cited by 177 publications

(96 citation statements)

References 9 publications

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“…In Dynamic Multi-Swarm PSO (DMSPSO) by Liang and Suganthan [33], random re-definition of small-sized (3-5 particles) neighborhood occurs, thereby forcing exchange of information among the particles. Recently, the modified DM-SPSO with exploitation by Quasi-Newton method by Zhao et al [34]. achieved comparable to better results to other optimization algorithms in the large-scale optimization competition held at the CEC2008 conference.…”

Section: Improving Exploration and Exploitationmentioning

confidence: 90%

Particle Swarm Optimization-A Survey

Kameyama

2009

IEICE Trans. Inf. & Syst.

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SUMMARYParticle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems. key words: particle swarm optimization, swarm intelligence

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Section: Improving Exploration and Exploitationmentioning

confidence: 90%

Particle Swarm Optimization-A Survey

Kameyama

2009

IEICE Trans. Inf. & Syst.

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“…It has shown better performance on multi-modal problems but fails on making an efficient local search. A new DMS-PSO, incorporated with a quasi-newton method, is proposed here [37] for Large Scale Global Optimization problems. This quasi-newton method improves the local search ability of DMS-PSO.…”

Section: Memetic Algorithmsmentioning

confidence: 99%

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Singh¹,

Jana²

2017

IJCA

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In recent years, there has been a remarkable improvement in the computing power of computers. As a result, numerous realworld optimization problems in science and engineering, possessing very high dimensions, have appeared. In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. Broadly these algorithms can be categorized in 3 groups: Standard Evolutionary Algorithms, Cooperative Co-evolution (CC) based Evolutionary Algorithms and Memetic Algorithms. This paper gives a brief introduction of some state-of-the-art Metaheuristics used in the field of LSGO, discusses their performance in CEC Competition on LSGO and finally, future scope in this field is presented.

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“…It is used for preventing from trapping in local optimums. The same structure is used in [3,4] however a local search that employs Quasi-Newton method is used in it. In [4] this structure is applied for large-scale optimization.…”

Section: Introductionmentioning

confidence: 99%

“…The same structure is used in [3,4] however a local search that employs Quasi-Newton method is used in it. In [4] this structure is applied for large-scale optimization. Hanning Chen et all [5] designed hierarchical structure called hierarchical swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

A New Hierarchical Structure for Combining Different Versions of PSO

Roshanzamir¹,

Mozayani²,

Roshanzamir³

2014

IJCA

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Particle swarm optimization is a population-based algorithm and used for optimization in a wide range of problems. In this article, a method that is called Hybrid Particle Swarm Optimization or HPSO is proposed. It is composed of some versions of particle swarm optimization algorithms, which have subgroups in their structures. They are DMS-PSO, PS2OS and MCPSO. In fact, a hierarchical structure is used to compose a new version of optimization algorithm and combine the results of other structures of PSO. Proposed structure has been tested on four unimodal and four multimodal test functions. Although the memory usage has no difference with other compared versions, it is much faster in many cases. Also the rank of fitness values, are good and suitable in all test functions. In addition, it is possible to execute it concurrently.

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Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization

Cited by 177 publications

References 9 publications

Particle Swarm Optimization-A Survey

Particle Swarm Optimization-A Survey

A Survey on Metaheuristics for Solving Large Scale Optimization Problems

A New Hierarchical Structure for Combining Different Versions of PSO

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