Large-scale many-objective particle swarm optimizer with fast convergence based on Alpha-stable mutation and Logistic function

As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.

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An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

Mathematics ◽

10.3390/math7050414 ◽

2019 ◽

Vol 7 (5) ◽

pp. 414 ◽

Cited By ~ 3

Author(s):

Weian Guo ◽

Lei Zhu ◽

Lei Wang ◽

Qidi Wu ◽

Fanrong Kong

Keyword(s):

Large Scale ◽

Optimization Problems ◽

Particle Swarm ◽

Large Scale Optimization ◽

Particle Swarm Optimizer ◽

Fitness Evaluation ◽

Diversity Maintenance ◽

Entropy Measurement ◽

Benchmark Suite ◽

Scale Optimization

Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

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A Guided Particle Swarm Optimizer for Distributed Operation of Electric Vehicle to Building Integration

Volume 2A: 43rd Design Automation Conference ◽

10.1115/detc2017-67530 ◽

2017 ◽

Cited By ~ 2

Author(s):

Yang Chen ◽

Mengqi Hu

Keyword(s):

Private Information ◽

Large Scale ◽

Particle Swarm ◽

Commercial Building ◽

Particle Swarm Optimizer ◽

Charging Station ◽

Solution Strategies ◽

Ev Charging ◽

Marginal Price ◽

Distributed Decision

Relevant research has demonstrated that more potential benefits can be achieved when energy and information are transacted and exchanged locally among different energy consumers. With increasing number of electric vehicles (EVs), various models and solution strategies have been developed for collaboration between building and EV charging station to achieve greater energy efficiency. However, most of the existing research employs centralized decision model which is time consuming for large scale problems and cannot protect private information for each participator. To bridge these research gaps, a guided particle swarm optimizer based distributed decision approach is proposed to study the energy transaction between building and EV charging station. In the proposed decision approach, the marginal price signal of transactive energy is collected to guide iterative direction of particle’s velocity and position which can maximally protect private information of building and EV charging station. A study case based on a commercial building and a nearby charging station in Chicago area is designed for illustration. The experimental results demonstrate that our proposed marginal price guided particle swarm optimizer is more stable and efficient comparing with canonical particle swarm optimizer and two state-of-the-art distributed decision algorithms.

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Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions

Engineering Optimization ◽

10.1080/03052150903247736 ◽

2010 ◽

Vol 42 (5) ◽

pp. 431-451 ◽

Cited By ~ 20

Author(s):

Shu-Kai S. Fan ◽

Ju-Ming Chang

Keyword(s):

Global Optimization ◽

Large Scale ◽

Particle Swarm ◽

Particle Swarm Optimizer ◽

Cluster Systems ◽

Pc Cluster ◽

Multimodal Functions

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Super-High Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.532-533.1830 ◽

2012 ◽

Vol 532-533 ◽

pp. 1830-1835

Author(s):

Ying Zhang ◽

Bo Qin Liu ◽

Han Rong Chen

Keyword(s):

High Dimension ◽

Large Scale ◽

Particle Swarm ◽

Search Space ◽

Scale Space ◽

Global Optimum ◽

Particle Swarm Optimizer ◽

Local Optima ◽

Complex Functions ◽

Global Optima

Due to the existence of large numbers of local and global optima of super-high dimension complex functions, general Particle Swarm Optimizer (PSO) methods are slow speed on convergence and easy to be trapped in local optima. In this paper, an Adaptive Particle Swarm Optimizer(APSO) is proposed, which employ an adaptive inertia factor and dynamic changes strategy of search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the proposed algorithm and compare it with other published methods on several super-high dimension complex functions, the experimental results demonstrate that this revised algorithm can rapidly converge at high quality solutions.

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