Introducing languid particle dynamics to a selection of PSO variants

Družeta, Siniša; Ivić, Stefan; Grbčić, Luka; Lučin, Ivana

doi:10.1016/j.eij.2019.11.005

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…The languid particle dynamics modification [ 66 ] was used which involves setting the inertia of a particle to zero if it is not moving in the direction of better fitness. This modification was used as it proved beneficial to the standard PSO algorithm on the problem of water distribution pipe network routing [ 67 ]. The PSO implementation in the python numerical optimization module indago 0.1.2. was also used.…”

Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.

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Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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“…where V i is the volume of the i th stiffener; n is the total number of the stiffeners; σ max is the allowed maximum von Mises stress for both shell elements and stiffener elements; σ is the material yield strength; w and h are the design variables; and w, w, h, and h are the lower and upper bounds of the stiffener width and height. Several optimization methods, such as the genetic algorithm [22][23][24] and particle swarm optimization [25][26][27], can be used to solve the above minimization problem. In this paper, the nonlinear constrained problem formulated in Equation ( 19) was solved with the interior point method in the MATLAB Optimization Toolbox, where the gradients are estimated using finite differences.…”

Section: Optimization Designmentioning

confidence: 99%

Optimized Unidirectional and Bidirectional Stiffened Objects for Minimum Material Consumption of 3D Printing

et al. 2021

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3D printing, regarded as the most popular additive manufacturing technology, is finding many applications in various industrial sectors. Along with the increasing number of its industrial applications, reducing its material consumption and increasing the strength of 3D printed objects have become an important topic. In this paper, we introduce unidirectionally and bidirectionally stiffened structures into 3D printing to increase the strength and stiffness of 3D printed objects and reduce their material consumption. To maximize the advantages of such stiffened structures, we investigated finite element analysis, especially for general cases of stiffeners in arbitrary positions and directions, and performed optimization design to minimize the total volume of stiffened structures. Many examples are presented to demonstrate the effectiveness of the proposed finite element analysis and optimization design as well as significant reductions in the material costs and stresses in 3D printed objects stiffened with unidirectional and bidirectional stiffeners.

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“…Based on utilizing particle-wise inertia control for resolving personal fitness improvement dependent particle movement, the PFIDI approach and the LPSO method [3,4] were proposed in which each particle tracks its fitness evolution so that this information can be used for altering its movement process. This information is then used in a switch-like condition on inertia term of each individual particle:…”

Section: Pso and Languid Particle Dynamicsmentioning

confidence: 99%

“…This means that the k-th particle has inertia only as long as it keeps advancing in a direction of better fitness (the formulation (3) assumes a minimization problem). The correction of +0.05 for inertia weight when inertia is not disabled was proposed in [4] so as to compensate for the reduced overall velocity of the swarm due to the intermittent inertial velocity of the particles. Behaving in this manner, a particle disregards its previous direction if it failed to take it to a better location.…”

Section: Pso and Languid Particle Dynamicsmentioning

confidence: 99%

“…In an attempt to enhance the effectiveness of PSO by enabling the particles with a certian "awareness" of their own improvement, we have previously proposed an enhancement of PSO method with Personal Fitness Improvement Dependent Inertia (PFIDI) which makes each particle's movement conditioned by its improvement in fitness. The proposed PFIDI technique of moving particles through the search space is called Languid Particle Dynamics (LPD) and the corresponding LPSO variant is called Languid PSO (LPSO) [3,4]. Tested on a spectrum of several PSO variants, method parameter configurations, and goal functions, the LPD technique was shown to predominantly produce an increase in PSO accuracy and stability.…”

Section: Introductionmentioning

confidence: 99%

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Anakatabatic Inertia: Particle-wise Adaptive Inertia for PSO

Družeta,

Ivić

2020

Preprint

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Throughout the course of the development of Particle Swarm Optimization, particle inertia has been established as an important aspect of the method for researching possible method improvements. As a continuation of our previous research, we propose a novel generalized technique of inertia weight adaptation based on individual particle's fitness improvement, called anakatabatic inertia. This technique allows for adapting inertia weight value for each particle corresponding to the particle's increasing or decreasing fitness, i.e. conditioned by particle's ascending (anabatic) or descending (katabatic) movement. The proposed inertia weight control framework was metaoptimized and tested on the 30 test functions of the CEC 2014 test suite. The conducted procedure produced four anakatabatic models, two for each of the PSO methods used (Standard PSO and TVAC-PSO). The benchmark testing results show that using the proposed anakatabatic inertia models reliably yield moderate improvements in accuracy of Standard PSO (final fitness minimum reduced up to 0.09 orders of magnitude) and rather strong improvements for TVAC-PSO (final fitness minimum reduced up to 0.59 orders of magnitude), mostly without any adverse effects on the method's performance.

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Introducing languid particle dynamics to a selection of PSO variants

Cited by 6 publications

References 24 publications

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Optimized Unidirectional and Bidirectional Stiffened Objects for Minimum Material Consumption of 3D Printing

Anakatabatic Inertia: Particle-wise Adaptive Inertia for PSO

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