A review of artificial fish swarm algorithms: recent advances and applications

Pourpanah, Farhad; Wang, Ran; Lim, Chee Peng; Wang, Xizhao; Yazdani, Danial

doi:10.1007/s10462-022-10214-4

Cited by 55 publications

(35 citation statements)

References 125 publications

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“…The artificial fish (AF) swarm algorithm was first proposed in 2002 and has been widely used in multi‐objective optimization problems, 38 such as resource allocation and scheduling. AF is a new bottom‐up optimization mode that can obtain a sub‐optimal or optimal solution to the problem in the solution space by simulating the feeding activities of fish swarms.…”

Section: The Proposed Af Meta‐heuristicmentioning

confidence: 99%

A container deployment strategy for server clusters with different resource types

Ouyang

Bai

et al. 2023

Concurrency and Computation

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The method of deploying microservices based on container technology is widely used in cloud environments. This method can realize the rapid deployment of microservices and improve the resource utilization of cloud datacenters. However, resource allocation and deployment of container-based microservices are key issues. With the continuous growth of computing-and storage-intensive services, it is necessary to consider the deployment of microservices of different business types. This study establishes a multi-objective optimization problem model with the similarity between containers and servers, load balance of clusters, and reliability of microservice execution as the optimization objectives. An improved artificial fish swarm algorithm is proposed for the container deployment of computing-and storage-intensive microservices. The comprehensive experimental results show that, compared with the existing deployment strategies, the matching degree between the container and server, cluster load balance value, service execution reliability, and other performance parameters are improved while shortening the running time of the algorithm. In addition, under the constraint of load balancing, the resource utilization of the computing and storage server clusters is improved.

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Section: The Proposed Af Meta‐heuristicmentioning

confidence: 99%

A container deployment strategy for server clusters with different resource types

Ouyang

Bai

et al. 2023

Concurrency and Computation

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“…The so-called group intelligence includes the behaviors of simple individuals and the whole group, exhibiting a specific intelligent feature without centralized control. Typical examples include ACO algorithm, PSO algorithm, artificial bee colony algorithm (ABC) [20], artificial fish swarming algorithm [21], grey wolf optimizer (GWO) [22], firefly algorithm (FA) [23], cuckoo search algorithm [24], chimp optimization algorithm [25], grasshopper optimization algorithm (GOA) [26], slime mold algorithm [27], and whale optimization algorithm (WOA) [28].…”

Section: Figure 1: Classification Of Metaheuristic Algorithmsmentioning

confidence: 99%

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Li¹,

Du²,

Zhang³

et al. 2023

Computers, Materials &Amp; Continua

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Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA's capability, the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms, including artificial bee colony algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm, grasshopper optimization algorithm, and whale optimization algorithm. The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems. DFA is applied to five engineering projects to demonstrate its applicability. The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms. Finally, potential upgrading routes for DFA are proposed as possible future developments.

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“…In order to solve the optimization challenge, random and parallel searching algorithms are used to mimic fish behavior such as following and swarming in order to find the best possible solution for the entire population as in Figure 2. In AFSA, fish have three behaviors in eating: preying, swarming, and following [24]. and intelligence of swarms in the process of finding food.…”

Section: Modified Artificial Fish Swarm Algorithm (Afsa)mentioning

confidence: 99%

“…Figure2. The relationship between an artificial fish and its surroundings "Reprinted/adapted with permission from Ref [24]…”

mentioning

confidence: 99%

A Hierarchical Deep Learning-Based Intrusion Detection Architecture for Clustered Internet of Things

Elsayed

Hamada

Hammoudeh

et al. 2022

JSAN

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The Internet of Things (IoT) system’s ever-expanding attack surface calls for a new intrusion detection system (IDS). These systems may include thousands of wireless devices that need to be protected from cyberattacks. Recent research efforts used machine learning to analyze and identify various attacks and abnormal behavior on IoT systems. Most of these techniques are characterized by low accuracy and they do not scale to today’s IoT-enabled smart cities applications. This article proposes a secure automatic two-levels intrusion detection system (SATIDS) which utilizes the minimum redundancy maximum relevance (MRMR) feature selection technique and an enhanced version of long short-term memory (LSTM) based on an artificial recurrent neural network (RNN) to enhance the IDS performance. SATIDS aims at detecting traffic anomalies with greater accuracy while also reducing the time it takes to perform this task. The proposed algorithm was trained and evaluated using two of the most recent datasets based on realistic data: ToN-IoT and InSDN datasets. The performance analysis of the proposed system proves that it can differentiate between attacks and normal traffic, identify the attack category, and finally define the type of sub-attack with high accuracy. Comparing the performance of the proposed system with the existing IDSs reveals that it outperforms its best rivals from the literature in detecting many types of attacks. It improves accuracy, detection rates, F1-score, and precision. Using 500 hidden and two LSTM layers achieves accuracy of 97.5%, precision of 98.4%, detection rate of 97.9%, and F1-score of 98.05% on ToN-IoT dataset, and precision of 99%, detection rate of 99.6%, and F1-score of 99.3% on InSDN dataset. Finally, SATIDS was applied to an IoT network which utilizes the energy harvesting real-time routing protocol (EHRT). EHRT optimizes the low-energy adaptive clustering hierarchy (LEACH) routing technique using a modified artificial fish swarm algorithm. The integration between the optimized LEACH and the proposed IDS enhances the network lifetime, energy consumption, and security.

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A review of artificial fish swarm algorithms: recent advances and applications

Cited by 55 publications

References 125 publications

A container deployment strategy for server clusters with different resource types

A container deployment strategy for server clusters with different resource types

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

A Hierarchical Deep Learning-Based Intrusion Detection Architecture for Clustered Internet of Things

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