scholarly journals An Advanced Chemical Reaction Optimization Algorithm Based on Balanced Local and Global Search

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Min Zhang ◽  
Liang Chen ◽  
Xin Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Chemical Reaction ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Superior Performance ◽  
Chemical Reaction Optimization ◽  
Swarm Optimization ◽  
Reaction Optimization ◽  
Comparison Results

An advanced chemical reaction optimization algorithm based on balanced local search and global search is proposed, which combines the advantages of adaptive chemical reaction optimization (ACRO) and particle swarm optimization (PSO), to solve continuous optimization problems. This new optimization is mainly based on the framework of ACRO, with PSO’s global search operator applied as part of ACRO’s neighborhood search operator. Moreover, a “finish” operator is added to the ACRO’s structure and the search operator is evolved by an adaptive scheme. The simulation results tested on a set of twenty-three benchmark functions, and a comparison was made with the results of a newly proposed hybrid algorithm based on chemical reaction optimization (CRO) and particle swarm optimization (denoted as HP-CRO). The finial comparison results show the superior performance improvement over HP-CRO in most experiments.

EMI-Based Damage Identification for Beam Structures

2014 ◽  
Vol 1081 ◽  
pp. 358-362 ◽  
Author(s):  
Yu Xiang Zhang ◽  
Jian Hai Yang ◽  
Fu Hou Xu ◽  
Jia Zhao Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Damage Identification ◽  
Particle Swarm ◽  
Global Search ◽  
Search Technique ◽  
Swarm Optimization ◽  
Identification Method ◽  
Basic Particle

A damage identification method is proposed to identify the damage style and the damage parameters. By driving a pair of PZT patches out phase and in phase, the electric admittance of the PZT is obtained. The damage parameters are then identified from the changes of the admittance spectra caused by the appearance of damage. By comparing the identification result, the damage style can be determined and the damage parameters can be obtained. The middle basic particle swarm optimization algorithm is employed as a global search technique to back-calculate the damage. Experiments are carried out on beams. The results demonstrate that the proposed method is able to identify the damage style, and can effectively and reliably locate and quantify the damage in the beam.

Edge reconstructions of black phosphorene: a global search

Nanoscale ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 4085-4091
Author(s):  
Yue Liu ◽  
Da Li ◽  
Tian Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Swarm Optimization ◽  
Black Phosphorene

A global search of black phosphorene edge structures are performed based on the particle swarm optimization algorithm.

scholarly journals A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Weitian Lin ◽  
Zhigang Lian ◽  
Xingsheng Gu ◽  
Bin Jiao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Fine Tuning ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Large Size ◽  
Swarm Algorithm

Particle swarm optimization algorithm (PSOA) is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA), and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA). Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

scholarly journals REDUCTION OF REAL POWER LOSS BY ADVANCED PARTICLE SWARM OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (2) ◽  
pp. 166-181
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Test System ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Real Power

This paper presents Advanced Particle Swarm Optimization (APSO) algorithm for solving optimal reactive power problem. In this work Biological Particle swarm Optimization algorithm utilized to solve the problem by eliminating inferior population & keeping superior population, to make full use of population resources and speed up the algorithm convergence. Projected Advanced Particle Swarm Optimization (APSO) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed Advanced Particle Swarm Optimization (APSO) algorithm in reducing the real power loss and static voltage stability margin (SVSM) Index has been enhanced.

scholarly journals A multi-sample particle swarm optimization algorithm based on electric field force

2021 ◽  
Vol 18 (6) ◽  
pp. 7464-7489
Author(s):  
Shangbo Zhou ◽  
◽  
Yuxiao Han ◽  
Long Sha ◽  
Shufang Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Field ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Field Force ◽  
Comparison Results ◽  
Electric Field Force

<abstract><p>Aiming at the premature convergence problem of particle swarm optimization algorithm, a multi-sample particle swarm optimization (MSPSO) algorithm based on electric field force is proposed. Firstly, we introduce the concept of the electric field into the particle swarm optimization algorithm. The particles are affected by the electric field force, which makes the particles exhibit diverse behaviors. Secondly, MSPSO constructs multiple samples through two new strategies to guide particle learning. An electric field force-based comprehensive learning strategy (EFCLS) is proposed to build attractive samples and repulsive samples, thus improving search efficiency. To further enhance the convergence accuracy of the algorithm, a segment-based weighted learning strategy (SWLS) is employed to construct a global learning sample so that the particles learn more comprehensive information. In addition, the parameters of the model are adjusted adaptively to adapt to the population status in different periods. We have verified the effectiveness of these newly proposed strategies through experiments. Sixteen benchmark functions and eight well-known particle swarm optimization algorithm variants are employed to prove the superiority of MSPSO. The comparison results show that MSPSO has better performance in terms of accuracy, especially for high-dimensional spaces, while maintaining a faster convergence rate. Besides, a real-world problem also verified that MSPSO has practical application value.</p></abstract>

A MUTATION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR MULTILAYER PERCEPTRON TRAINING WITH APPLICATIONS

2013 ◽  
Vol 09 (01) ◽  
pp. 1350003
Author(s):  
ZENGHAI CHEN ◽  
JING WU ◽  
ZHERU CHI
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Multilayer Perceptron ◽  
Back Propagation ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Superior Performance ◽  
Mutation Operator ◽  
Classification Problems ◽  
Swarm Optimization

Particle swarm optimization (PSO), a prevalent optimization algorithm, has been successfully applied to various fields of science and engineering. However, PSO still suffers from some problems such as premature convergence. To solve these problems, we propose a mutation PSO (MPSO) in this paper. Compared with the traditional PSO, there are two main improvements of the proposed MPSO. First, a new particle update rule is explored. The new rule updates a particle's position according to not only its best known position and the global best known position of the swarm, but also a number of other particles' best known positions. The second improvement is that a mutation operator is employed. Mutation operator is used to avoid premature convergence. The MPSO is utilized to train a multilayer perceptron (MLP). The MLP trained by MPSO is finally applied to two classification problems: Iris flower classification and scene classification. For comparison purposes, traditional PSO, genetic algorithm (GA), and back-propagation (BP) are also investigated. Experimental results demonstrate the superior performance of the proposed MPSO for MLP training.

scholarly journals UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jian Zhang ◽  
Jianan Sheng ◽  
Jiawei Lu ◽  
Ling Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
And Performance

The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

scholarly journals A Chaotic Hybrid Butterfly Optimization Algorithm with Particle Swarm Optimization for High-Dimensional Optimization Problems

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1800
Author(s):  
Mengjian Zhang ◽  
Daoyin Long ◽  
Tao Qin ◽  
Jing Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
One Dimensional ◽  
Comparison Results ◽  
Dimensional Optimization

In order to solve the problem that the butterfly optimization algorithm (BOA) is prone to low accuracy and slow convergence, the trend of study is to hybridize two or more algorithms to obtain a superior solution in the field of optimization problems. A novel hybrid algorithm is proposed, namely HPSOBOA, and three methods are introduced to improve the basic BOA. Therefore, the initialization of BOA using a cubic one-dimensional map is introduced, and a nonlinear parameter control strategy is also performed. In addition, the particle swarm optimization (PSO) algorithm is hybridized with BOA in order to improve the basic BOA for global optimization. There are two experiments (including 26 well-known benchmark functions) that were conducted to verify the effectiveness of the proposed algorithm. The comparison results of experiments show that the hybrid HPSOBOA converges quickly and has better stability in numerical optimization problems with a high dimension compared with the PSO, BOA, and other kinds of well-known swarm optimization algorithms.

scholarly journals A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Zhigang Lian
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Job Shop ◽  
Particle Swarm Optimization Algorithm ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Global Search ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems

The Job-shop scheduling problem (JSSP) is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA), generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA) is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

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