Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms

Ma, Zhongqiang; Wu, Guohua; Suganthan, P. N.; Song, Aijuan; Luo, Qizhang

doi:10.1016/j.swevo.2023.101248

Cited by 69 publications

(30 citation statements)

References 98 publications

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“…However, since our research is not focused on image or video recognition, we have chosen the basic ANN approach for this study. There are many nature-inspired algorithms that can be used for hyperparameter optimization, for example, there are more than 500 listed in [30]. Some of them are well-researched classical nature-inspired algorithms such as genetic algorithms or ant colony optimization, while many newer algorithms have been studied in recent years such as Harris Hawk Optimization [31], Grey Wolf Optimization, and FireFly Optimization.…”

Section: Proposed Intrusion Detection System For Uav Communicationmentioning

confidence: 99%

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

Cengiz,

Lipsa,

Dash

et al. 2024

IEEE Access

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Unmanned aerial vehicles (UAVs) are increasingly being deployed in crucial missions for the armed forces, law enforcement, industrial control monitoring, and other sectors. However, these hostile operating circumstances, along with the UAVs' dependence on wireless protocols, pose substantial security threats, limiting their mainstream application. With network security being such a major issue for UAV networks, the machine learning-based intrusion detection system (IDS) has been determined to be an effective strategy for protecting them. Additionally, though the existing methods offer effective strategies for detecting and categorizing abnormalities in the system, they are limited by their inability to adjust to various attack patterns. The dataset used as well as the memory and computational requirement of existing models, poses new challenges. One of the main concerns pertains to the reduced computational and memory demands of these models. So, the work carried out in this paper addresses this challenge. The work carried out in this paper addresses this challenge. A new dimensional reduction technique based on correlation coefficient, information gain, and principal component analysis (PCA) is introduced to reduce the dimensionality of the UAV Attack Dataset. A novel intrusion detection system based on an artificial neural network (ANN) and genetic algorithm (GA) is then proposed. The genetic algorithm is used to generate the optimal weights of the artificial neural network. A comparison is made between the proposed model and the backpropagation network and its variant in terms of its convergence and prediction accuracy. Furthermore, the performance of the proposed model is compared with that of other classifiers. This comparison reveals that the proposed model is time efficient with an increased prediction accuracy of at least 6% more than other classifiers.

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Section: Proposed Intrusion Detection System For Uav Communicationmentioning

confidence: 99%

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

Cengiz,

Lipsa,

Dash

et al. 2024

IEEE Access

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show abstract

“…We aim at comparison between a large number of methods, including both the best algorithms that we were aware of, and many less known ones. We have, though, tried to avoid metaphor-based methods in which novelty or performance could be doubtful -as discussed in landmark critical papers [43], [149]. However, we are aware that many widely appreciated, popular methods are anyway not included in the present test.…”

Section: Evolutionary Algorithms Comparedmentioning

confidence: 99%

How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?

2023

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Hundreds of variants of Swarm Intelligence or Evolutionary Algorithms are proposed each year and numerous competitions and comparisons between algorithms may suggest rapid improvement in the field. However, such comparisons are often done between a limited number of methods and are based on averaged ranks of algorithms. This way they measure whether one method is on average ranked better than the others, without giving any information on how much improvement is in fact obtained. In this study we show a general comparison between 69 algorithms, starting from methods proposed in the 1960's up to variants developed in the early 2020's, on single-objective static numerical problems. Algorithms are compared on searching for a minimum of 30 different 50-dimensional mathematical functions, and on 22 real-world problems. We focus on the relative improvement achieved by various algorithms over a single-solution based method proposed in 1960 by Howard Rosenbrock. We find that the general improvement of Evolutionary Algorithms over Rosenbrock's algorithm is relatively limited. It is high for the artificial benchmarks, for which many Evolutionary Algorithms find solutions 10 times closer to the global optimum in terms of fitness than Rosenbrock's algorithm, but much lower for real-world problems. Improvement is also higher when performance averaged over many runs is compared, but lower when the best results from multiple runs are analyzed. In the last case, only the best Evolutionary Algorithms are able to find solutions of a ''typical'' real-world problem that are 2-3 times better in terms of fitness than those found by Rosenbrock's algorithm. The relative improvement of recently proposed algorithms is not much better than the improvement achieved by algorithms proposed over a decade ago.

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“…For particles learned from g P , the velocity will be updated from Equation (7); For particles learned from Candidate , the velocity will be updated from Equation (8).…”

Section: Candidate Particle Generation Strategy Based On Potential Op...mentioning

confidence: 99%

“…POA has more branches, such as swarm intelligence (SI) optimization algorithms, evolutionary algorithms (EA), physics/chemistry-based algorithms (P/CBA), etc. [8]. Among the meta-heuristic optimization algorithms that have emerged in the past, PSO is one of the most popular [9].…”

Section: Introductionmentioning

confidence: 99%

Hybrid particle swarm optimization algorithm based on arena competitive learning strategy

Lu,

Wang,

Wang

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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The research on optimization algorithms has kept pace with the times, and one of the hot spots that has attracted much attention is the improvement of Particle Swarm Optimization algorithm (PSO) in the field of swarm intelligence. In this paper, a hybrid PSO algorithm using Adaptive Learning strategy (ALPSO) is introduced, and a hybrid PSO based on Arena Competitive Learning strategy (ACLPSO) is proposed from the perspective of search space and candidate particle generation, effectively enriches population diversity, expands the initial particle swarm search space, and improves convergence speed and accuracy.

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Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms

Cited by 69 publications

References 98 publications

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?

Hybrid particle swarm optimization algorithm based on arena competitive learning strategy

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