A Survey on Cat Swarm Optimization Algorithm

Ihsan, Rasheed Rebar; Almufti, Saman M.; Ormani, Bijar M. S.; Asaad, Renas Rajab; Marqas, Ridwan Boya

doi:10.9734/ajrcos/2021/v10i230237

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…In contrast to iterative or optimization techniques, metaheuristics do not ensure that the optimum solution for a given class of problems can be identified. Numerous metaheuristics use stochastic optimization in some way, which means that the solution is based on the set of produced random variables (Ihsan et al, 2021). Metaheuristics may frequently identify good solutions in combinatorial optimization with less computing work than optimization algorithms, iterative techniques, or basic heuristics since they search through a vast range of viable alternatives.…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Overview of Metaheuristic Algorithms

Almufti

Shaban²,

Ali³

et al. 2023

PGSRT

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Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.

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Section: Metaheuristic Algorithmsmentioning

confidence: 99%

Overview of Metaheuristic Algorithms

Almufti

Shaban²,

Ali³

et al. 2023

PGSRT

View full text Add to dashboard Cite

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“…Namely, tracing and seeking modes. CSO can present better performance [21]. The CSO algorithm was improved by some researchers to enhance its efficiency [42], [41].…”

Section: Motivationsmentioning

confidence: 99%

Hybrid Cat Swarm Optimization with Genetic Algorithm to solve the Open Shop Scheduling with Vehicle Routing Problem

Kekli

Nouri

Driss

2023

Preprint

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Supply chain management consists of optimising production process from raw material sourcing to final product, logistics and delivery to the final customer. In this context, we propose for the first time the hybridization of two metaheuristic algorithms, the cat swarm optimization and the genetic algorithm, for a simultaneous resolution of the open shop scheduling problem with the vehicle routing problem. The objective function is to minimize the end date of workshop process called makespan and the total traveled distance by the vehicles. The computational experiments were performed based on well-known benchmark data instances. The generated results were compared with the best approaches of the literature, in terms of generated solutions and processing time, in order to show the effectiveness of the proposed approach.

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“…They are constantly alertly observing their surroundings, and whenever they spot a target, they begin moving quickly in that direction. These two traits of cats have been combined to create the CSO algorithm [22].…”

Section: The Cat Swarm Optimizationmentioning

confidence: 99%

Cat swarm optimization-based energy efficient target tracking in wireless sensor network

Tabatabaei¹

2023

Preprint

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Objective: using the cat swarm optimization algorithm to reduce energy consumption and data transmission delay in WSNs (wireless sensor networks) Materials and Methods: WSNs have emerged as a critical sensing technology with a wide range of military and civilian applications. In these networks, a large number of sensors are distributed throughout an area to track a specific target. Each sensor is regarded as an operating tool that uses limited energy. They can sense and understand events within their sensing domains, as well as communicate with neighbouring sensor nodes. Because the battery determines the lifespan of a sensor, an efficient method of energy consumption must be used. The depletion of batteries' limited energy reserves may result in the network's demise. As a result, devising and developing a time plan for sensors' sleep and wake-up cycles is regarded as a critical goal in line with increasing network lifetime. We proposed a novel and different time planning method for enhancing the lifetimes of WSNs in this paper, using the cat swarm optimization algorithm, in which at least half of the full coverage is provided at all times. The simulation results showed that using a cat swarm optimization algorithm can reduce energy consumption and data transmission delay. It also improves load balance. Opnet simulator version 11.5 was used for the experiments and simulation.s

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A Survey on Cat Swarm Optimization Algorithm

Cited by 13 publications

References 37 publications

Overview of Metaheuristic Algorithms

Overview of Metaheuristic Algorithms

Hybrid Cat Swarm Optimization with Genetic Algorithm to solve the Open Shop Scheduling with Vehicle Routing Problem

Cat swarm optimization-based energy efficient target tracking in wireless sensor network

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