A Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting

Huang, Chao-Ming; Huang, Chun-Ta; Wang, Mingli

doi:10.1109/tpwrs.2005.846106

Cited by 225 publications

(86 citation statements)

References 19 publications

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“…Finally, we can indicate that the last term of (21), added in the conventional PSO velocity update (17), displays the information transferred via passive congregation of particle-with a randomly selected particle-. This passive congregation operator can be regarded as a stochastic variable that introduces perturbations to the search process.…”

Section: Passive Congregation-based Psomentioning

confidence: 99%

A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems

Vlachogiannis¹,

Lee

2006

IEEE Trans. Power Syst.

225

108

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Abstract-In this paper, three new particle swarm optimization (PSO) algorithms are compared with the state of the art PSO algorithms for the optimal steady-state performance of power systems, namely, the reactive power and voltage control. Two of the three introduced, the enhanced GPAC PSO and LPAC PSO, are based on the global and local-neighborhood variant PSOs, respectively. They are hybridized with the constriction factor approach together with a new operator, reflecting the physical force of passive congregation observed in swarms. The third one is based on a new concept of coordinated aggregation (CA) and simulates how the achievements of particles can be distributed in the swarm affecting its manipulation. Specifically, each particle in the swarm is attracted only by particles with better achievements than its own, with the exception of the particle with the best achievement, which moves randomly as a "crazy" agent. The obtained results by the enhanced general passive congregation (GPAC), local passive congregation (LPAC), and CA on the IEEE 30-bus and IEEE 118-bus systems are compared with an interior point (IP)-based OPF algorithm, a conventional PSO algorithm, and an evolutionary algorithm (EA), demonstrating the excellent performance of the proposed PSO algorithms.

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Section: Passive Congregation-based Psomentioning

confidence: 99%

A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems

Vlachogiannis¹,

Lee

2006

IEEE Trans. Power Syst.

225

108

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“…No commercial tools are used. In order to test the performance of the proposed algorithm, the classical CS algorithm and PSO algorithm [27] are employed as the comparison. The initial parameters of the three algorithms are listed in Table 2.…”

Section: Parameters Identificationmentioning

confidence: 99%

Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

Ding

et al. 2018

Journal of Robotics

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This paper concerns the problem of dynamical identification for an industrial robot manipulator and presents an identification procedure based on an improved cuckoo search algorithm. Firstly, a dynamical model of a 6-DOF industrial serial robot has been derived. And a nonlinear friction model is added to describe the friction characteristic at motion reversal. Secondly, we use a cuckoo search algorithm to identify the unknown parameters. To enhance the performance of the original algorithm, both chaotic operator and emotion operator are employed to help the algorithm jump out of local optimum. Then, the proposed algorithm has been implemented on the first three joints of the ER-16 robot manipulator through an identification experiment. The results show that (1) the proposed algorithm has higher identification accuracy over the cuckoo search algorithm or particle swarm optimization algorithm and (2) compared to linear friction model the nonlinear model can describe the friction characteristic of joints better.

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“…Generally, the optimisation problems can be abstracted as [2]: (1) where denotes the objective function without considering constraints;…”

Section: Brief Description Of Psomentioning

confidence: 99%

Elitism set based particle swarm optimization and its application

Sun¹,

Wang²

2017

IJCIS

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Topology plays an important role for Particle Swarm Optimization (PSO) to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.

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A Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting

Cited by 225 publications

References 19 publications

A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems

A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems

Nonlinear Friction and Dynamical Identification for a Robot Manipulator with Improved Cuckoo Search Algorithm

Elitism set based particle swarm optimization and its application

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