Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed, Aram Mahmood; Rashid, Tarik A.; Saeed, Soran

doi:10.1155/2020/4854895

Cited by 100 publications

(28 citation statements)

References 99 publications

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“…Optimization [10], [11], Artificial Bee Colony [12], Ant Colony [13], Crow Search Optimization [14], Cat Swarm Optimization [15], Grey Wolf Optimization [16], Whale Optimization [17]. The advantages/disadvantages of these methods are all well documented in literature.…”

Section: Other Methods Available Include Particle Swarmmentioning

confidence: 99%

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Jiménez-Gutiérrez¹,

Sánchez‐Partida²,

Martínez‐Flores³

et al. 2020

Adv. sci. technol. eng. syst. j.

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A distribution company in Mexico covers the travel expenses for 21 sales representatives. Currently, the routes they follow are not established clearly, which can lead to high costs in this subject. A reduction of such cost is sought after, by optimizing the routes for each one of them. The following research finds an improvement on the routes for the sales representative of a distribution company in Mexico. It was done by using the Traveling Salesman Problem with Hotel Selection or Base selections via a Simulated Annealing algorithm. The results show an improvement in a reasonable timeframe by using the Simulated Annealing. It also shows that the maximum process time was of 156.63 minutes, and the least amount of improvement was 24.44% over the current route selection. Applying this model will be beneficial for the company as the company is trying to reduce costs related to the sales representatives such as; fuel cost, hotel cost, and travel expenses.

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Section: Other Methods Available Include Particle Swarmmentioning

confidence: 99%

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Jiménez-Gutiérrez¹,

Sánchez‐Partida²,

Martínez‐Flores³

et al. 2020

Adv. sci. technol. eng. syst. j.

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show abstract

“…f. Chicken Swarm Optimization Algorithm (CSOA) is a recent optimization algorithm that mimics the behaviors of the chicken swarm and their hierarchal order [55,62]. g. Cat Swarm Optimization (CSO) [63] is a Swarm Intelligence (SI) algorithm that was inspired by the natural behavior of cats. h. Moth Flame Optimization (MFO) is a novel nature-inspired optimization paradigm inspired by the navigation method of moths in nature called transverse orientation [64].…”

Section: Bioinspired Algorithms and Optimizationmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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“…Abualigah et al [22] has given a comprehensive survey on variants of the salp swarm optimization algorithm and its applications. Ahmed et al [23] has presented overview of cat swarm optimization algorithm and its variants. This algorithm is inspired by the resting and tracing behavior of cats.…”

Section: Introductionmentioning

confidence: 99%

A novel sketch based face recognition in unconstrained video for criminal investigation

Lakshmi¹,

Arakeri

2023

IJECE

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Face recognition in video surveillance helps to identify an individual by comparing facial features of given photograph or sketch with a video for criminal investigations. Generally, face sketch is used by the police when suspect’s photo is not available. Manual matching of facial sketch with suspect’s image in a long video is tedious and time-consuming task. To overcome these drawbacks, this paper proposes an accurate face recognition technique to recognize a person based on his sketch in an unconstrained video surveillance. In the proposed method, surveillance video and sketch of suspect is taken as an input. Firstly, input video is converted into frames and summarized using the proposed quality indexed three step cross search algorithm. Next, faces are detected by proposed modified Viola-Jones algorithm. Then, necessary features are selected using the proposed salp-cat optimization algorithm. Finally, these features are fused with scale-invariant feature transform (SIFT) features and Euclidean distance is computed between feature vectors of sketch and each face in a video. Face from the video having lowest Euclidean distance with query sketch is considered as suspect’s face. The proposed method’s performance is analyzed on Chokepoint dataset and the system works efficiently with 89.02% of precision, 91.25% of recall and 90.13% of F-measure.

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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cited by 100 publications

References 99 publications

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Simulated Annealing for Traveling Salesman Problem with Hotel Selection for a Distribution Company Based in Mexico

Applications and Challenges of Artificial Intelligence in Space Missions

A novel sketch based face recognition in unconstrained video for criminal investigation

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