Developing Shuffled Frog‐Leaping Algorithm (SFLA) Method to Solve Power Load‐Constrained TCRTO Problems in Civil Engineering

Tao, Xingyu; Li, Heng; Mao, Chao; Chen, Wang; Yap, Jeffrey Boon Hui; Sepasgozar, Samad M. E.; Shirowzhan, Sara; Rose, Timothy

doi:10.1155/2019/1404636

Cited by 6 publications

(1 citation statement)

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“…The main advantages of SFLA are that it is more powerful in solving complex combinatorial optimization problems, has a faster search capability, and is more robust in determining the global optimum because of the evolution of several memeplexes (the structure responsible for local exploration) and the scrambling process (the structure responsible for global exploration), which can improve the quality of individuals [ 36 ]. The pseudocode and more details are provided in [ 4 ].…”

Section: Architecturementioning

confidence: 99%

A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Castro

Salles

Ciarelli

2021

Sensors

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This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

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Section: Architecturementioning

confidence: 99%