Modification of Particle Swarm Optimization by Reforming Global Best Term to Accelerate the Searching of Odor Sources

Widiyanto, Didit; Purnomo, Dwi; Jati, Grafika; Mantau, Aprinaldi Jasa; Jatmiko, Wisnu

doi:10.21307/ijssis-2017-924

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Problem 1: Avoiding being trapped in local optima Due to the time‐varying odor dispersion in realistic environments, a problem faced by S‐PSO methods is being trapped in local optima [72]. Several variant PSO methods were introduced to maintain diversity of the positional distribution of robots and prevent them from being trapped in local optima, including the detection and responding PSO (DR‐PSO) [53], the charged PSO (C‐PSO) [53], the explorative PSO ( E ‐PSO) [54], the ignore global best PSO (IgB‐PSO) [59], the request and reset PSO (RR‐PSO) [55], and so on.…”

Section: Heuristic Searching Methodsmentioning

confidence: 99%

Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

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Accurate and prompt localization of hazardous release sources ensures a timely emergency response to potential damages. Robot odor source localization is the study of locating the release source based on mobile robots in real‐time, which has been experiencing rapid growth in recent years. This paper presents a review of this field, focusing on recent progress and development trends. Research works are grouped according to method principles, leading to four categories: reactive methods, heuristic searching methods, probabilistic inference methods, and learning methods. The odor‐source direction and distance prediction via in situ sensing and developments of simulators are also listed separately for discussion. Several outlooks are highlighted at the end. Along with the developments of related fields and practical needs, we believe this field is bound to keep on flourishing in the future. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Heuristic Searching Methodsmentioning

confidence: 99%

Recent Progress and Trend of Robot Odor Source Localization

Jing

Meng

Ishida

2021

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…Once the onlooker has selected its food source x i , it produces a modification on x i by using Eq. (12). As in the case of the employed bees, if the modified food source has a better or equal nectar amount than x i , the modified food source will replace x i and become a new member in the population.…”

Section: L(•) Function X Ij Is a Binary Value Mapping From The Associated Position Vector X I Through Function L(•)mentioning

confidence: 99%

“…So, obtaining the optimal feature subset and model parameters must occur simultaneously. In previous literature, some optimization techniques such as the genetic algorithm (GA) [9], simulated annealing (SA) [10], ant colony optimization, (ACO) [11] and particle swarm optimization (PSO) [12] were employed to tune the parameters of SVMs and INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL.9, NO.4, DECEMBER 2016 optimize the input feature subset. But these methods are easy to trap into local optimum and could not get the global solution.…”

Section: Introductionmentioning

confidence: 99%