Particle Swarm Optimization

Lazinica, Aleksandar

doi:10.5772/109

Cited by 97 publications

(3 citation statements)

References 28 publications

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“…A few percent of a statistical measurement error usually leads to a slight deterioration of results, but it is still enough to pre-estimate the number, shape and position of components. The continuous functions approach was tested using a gradient methods, exhaustive algorithm and Particle Swarm Optimization (PSO) algorithm [7]. In most cases (for complex distributions) the obtained results do not allow clearly determining the number of components, their position or shape.…”

Section: Resultsmentioning

confidence: 99%

Numerical Analysis of Time Dependent Effects in Bulk Nanocrystalline Hard Magnets

Ziółkowski

Chrobak

Randrianantoandro³

et al. 2012

SSP

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The presented work concerns numerical analysis of t-dependent magnetization curves. We propose the approach consisting in application of the so-called simulated annealing algorithm (SA) supplemented with local maximum entropy condition. Efficiency of the SA procedure and its parameters was tested with the use of computer generated curves derived from known distributions of relaxation time τ. Based on the determined SA parameters the analysis of empirical M(t) dependences measured for (Fe80Nb6B14)1-xTbx(x=0.08, 0.16) bulk nanocrystalline alloys were also carried out. As it was shown the SA procedure allows determining distributions of relaxation time without preliminary assumption about its shape and number of components.

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Section: Resultsmentioning

confidence: 99%

Numerical Analysis of Time Dependent Effects in Bulk Nanocrystalline Hard Magnets

Ziółkowski

Chrobak

Randrianantoandro³

et al. 2012

SSP

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“…Particle Swarm Optimization (PSO) is an evolutionary computation technique inspired by social behavior of groups like bird flocking, fish schooling or colonies of insects; because it is known that a group can effectively achieve an objective by using the common information of every element. PSO algorithm was first introduced in 1995 by Eberhart and Kennedy [25] as an alternative to population based search approaches (like genetic algorithms) in order to solve optimization problems.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Metaheuristic Optimization of PD and PID Controllers for Robotic Manipulators

Bounouara¹,

Ghanai²,

Chafaa³

2021

JESA

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In this paper, the Particle Swarm Optimization algorithm (PSO) is combined with Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) to design more efficient PD and PID controllers for robotic manipulators. PSO is used to optimize the controller parameters Kp (proportional gain), Ki (integral gain) and Kd (derivative gain) to achieve better performances. The proposed algorithm is performed in two steps: (1) First, PD and PID parameters are offline optimized by the PSO algorithm. (2) Second, the obtained optimal parameters are fed in the online control loop. Stability of the proposed scheme is established using Lyapunov stability theorem, where we guarantee the global stability of the resulting closed-loop system, in the sense that all signals involved are uniformly bounded. Computer simulations of a two-link robotic manipulator have been performed to study the efficiency of the proposed method. Simulations and comparisons with genetic algorithms show that the results are very encouraging and achieve good performances.

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“…) in a large domain. Many heuristic optimisation methods such as the particle swarm optimisation (Lazinica 2009, Kiranyaz et al, 2014 for example, incorporate a local search in the vicinity of a point, to find the local best value. Because within a relatively small vicinity of a point, the first-order partial derivatives do not normally change sign, the proposed technique can be used for determining quickly the extreme local value in the vicinity of a specified point.…”

mentioning

confidence: 99%

Reducing Risk Through Inversion and Self-Strengthening

Todinov

2017

International Journal of Risk and Contingency Management

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A number of new techniques for reliability improvement and risk reduction based on the inversion method, such as: ‘inverting design variables,' ‘inverting by maintaining an invariant,' ‘inverting resulting in a reinforcing counter-force,' ‘negating basic required functions' and ‘moving backwards to general and specific contributing factors' have been introduced for the first time. By using detailed calculations, it has been demonstrated how the new technique ‘repeated inversion maintaining an invariant' can be applied to reduce the risk of collision for multiple ships travelling at different times and with variable speeds. It has been demonstrated that for pressure vessels, an inversion of the geometric parameters by maintaining an invariant volume could result not only in an increased safety but also in a significantly reduced weight. The method of self-strengthening (self-reinforcement) has been introduced for the first time as a systematic method for improving reliability and reducing risk. The method of self-strengthening by capturing a proportional compensating factor and the method of self-strengthening by creating a positive feedback loop have been proposed for the first time as reliability improvement tools. Finally, classifications have been proposed of methods and techniques for risk reduction based on the methods of inversion and self-strengthening.

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Particle Swarm Optimization

Cited by 97 publications

References 28 publications

Numerical Analysis of Time Dependent Effects in Bulk Nanocrystalline Hard Magnets

Numerical Analysis of Time Dependent Effects in Bulk Nanocrystalline Hard Magnets

Metaheuristic Optimization of PD and PID Controllers for Robotic Manipulators

Reducing Risk Through Inversion and Self-Strengthening

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