Cultured Differential Particle Swarm Optimization for Numerical Optimization Problems

2007 ◽  
Author(s):  
Sheng Liu ◽  
Xingyu Wang ◽  
Xiaoming You
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization

Taguchi-Particle Swarm Optimization for Numerical Optimization

2012 ◽  
pp. 44-59
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

scholarly journals Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Li ◽  
Yifei Sun ◽  
Sicheng Hou
Keyword(s):  
Experimental Study ◽  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fixed Phase ◽  
Continuous Optimization Problems

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

scholarly journals Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 876 ◽  
Author(s):  
Ma ◽  
Yuan ◽  
Han ◽  
Sun ◽  
Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Chaotic Pso

As a global-optimized and naturally inspired algorithm, particle swarm optimization (PSO) is characterized by its high quality and easy application in practical optimization problems. However, PSO has some obvious drawbacks, such as early convergence and slow convergence speed. Therefore, we introduced some appropriate improvements to PSO and proposed a novel chaotic PSO variant with arctangent acceleration coefficient (CPSO-AT). A total of 10 numerical optimization functions were employed to test the performance of the proposed CPSO-AT algorithm. Extensive contrast experiments were conducted to verify the effectiveness of the proposed methodology. The experimental results showed that the proposed CPSO-AT algorithm converges quickly and has better stability in numerical optimization problems compared with other PSO variants and other kinds of well-known optimal algorithms.

A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

2014 ◽  
Vol 31 (7) ◽  
pp. 1198-1220 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir Hossein Gandomi ◽  
Xin-She Yang ◽  
Amir Hossein Alavi
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Content Type ◽  
Accelerated Particle Swarm Optimization

Purpose – Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach – The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings – A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO. Originality/value – A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

scholarly journals Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao Fu ◽  
Wangsheng Liu ◽  
Bin Zhang ◽  
Hua Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Studies ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Complex Optimization

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants.

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.

Taguchi-Particle Swarm Optimization for Numerical Optimization

2010 ◽  
Vol 1 (2) ◽  
pp. 18-33
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

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