A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search

Fan, Shu-Kai S.; Liang, Yun-Chia; Zahara, Erwie

doi:10.1016/j.cie.2005.01.022

Cited by 136 publications

(56 citation statements)

References 26 publications

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“…It is a simple basic algorithm and quite easy to use. This method is founded on a simplex which presents a geometrical figure composed of ( + 1) points in -dimension; segments will be connecting them and then polygonal surfaces will be resulting such as a segment on a line, a triangle in two-dimensional space, a tetrahedron for three-dimensional space [40].…”

Section: Identification Methodsmentioning

confidence: 99%

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

Khoja

Ladhari

Sakly

et al. 2018

Mathematical Problems in Engineering

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The current paper is entirely devoted to show the applicability of Particle Swarm Optimization (PSO) algorithm as a parameter identification method for a representative model of an Activated Sludge Wastewater Treatment Process (ASWWTP) with alternating phases. The model of identification is composed of two linear submodels: one for the aerobic phase and the other for the anoxic phase. In order to prove the efficiency of the proposed method, its performance is compared with another classical method called Simplex Search Algorithm (SSA) as well as with the experimental data.

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Section: Identification Methodsmentioning

confidence: 99%

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

Khoja

Ladhari

Sakly

et al. 2018

Mathematical Problems in Engineering

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“…In PSO algorithm, the potential solutions called "particles" fly around in the multidimensional Figure 9 The flow diagram of the algorithm for searching l m0 search space and the positions of individual particles are adjusted according to their own flying experience. In the first loop of main algorithm, an approximate minimum value of function gða 1 ; a 2 Þ is obtained by using the hybrid NM-PSO algorithm proposed by Fan et al (2006). They integrate NM method with PSO to locate the global optimal solution for the nonlinear continuous variable functions.…”

Section: Searching For the Value Of Amentioning

confidence: 99%

“…Paper by Hedar and Fukushima (2006) proposes a continuous Tabu search called directed Tabu search method. Paper by Fan et al (2006) integrates Nelder-Mead (NM) method with GA and particle swarm optimization (PSO), respectively. In this paper, a hybrid NM-PSO algorithm and a modified NM algorithm are used.…”

Section: Searching For the Value Of Amentioning

confidence: 99%

Forward kinematics analysis of a six‐DOF Stewart platform using PCA and NM algorithm

Wang

Shang

et al. 2009

Industrial Robot: An International Journal

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Purpose -The purpose of this paper is to present an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform. Design/methodology/approach -Unlike the convention of developing a set of kinematic equations and then solving them, an alternative numerical algorithm is proposed in which the principal components of link lengths are used as a bridge to analyze the forward kinematics of a Stewart platform. The values of link lengths are firstly transformed to the values of principal components through principal component analysis. Then, the computation of the values of positional variables is transformed to a two-dimensional nonlinear minimization problem by using the relationships between principal components and positional variables. A hybrid Nelder Mead-particle swarm optimizer (NM-PSO) algorithm and a modified NM algorithm are used to solve the two-dimensional nonlinear minimization problem. Findings -Simulation experiments have been conducted to validate the numerical algorithm and experimental results show that the numerical algorithm is valid and can achieve good accuracy and high efficiency. Originality/value -This paper proposes an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

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“…The crossover operator of a real-coded GAs is performed by the borrowing concept of convex combination. The random mutation operator is used to change the gene with a random number in the problem's domain [34,35].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

An AHP-GA-BP Algorithm for Evaluation of Enterprise Collaborative Innovation Management of Intellectual Property Rights

Deng¹,

Chaoming²

2014

IJUNESST

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Based on the theory of BP neural network, we use AHP method to construct the index system and standard of enterprise collaborative management of intellectual property rights under the view of innovation, and the specific evaluation indexes are given from five aspects. In this paper, in order to overcome the existing shortcomings of multi index system In the evaluation method, we use AHP-GA-BP neural network to comprehensive evaluation each index of enterprise intellectual property management. Our method not only displays the evaluation expert experience in learning The evaluation results show that this algorithm can effectively analyze enterprise intellectual property management problems.

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A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search

Cited by 136 publications

References 26 publications

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

Parameter Identification of an Activated Sludge Wastewater Treatment Process Based on Particle Swarm Optimization Method

Forward kinematics analysis of a six‐DOF Stewart platform using PCA and NM algorithm

An AHP-GA-BP Algorithm for Evaluation of Enterprise Collaborative Innovation Management of Intellectual Property Rights

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