A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Zhu, Haoran; Wang, Yunhe; Ma, Zhiqiang; Li, Xiangtao

doi:10.3390/math9020171

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…The hybrid gravitational-firefly algorithm uses two well-known optimization techniques, the GSA and the FA [35][36][37][38]. Newton's gravitational law is the working principle for the GSA.…”

Section: Hybrid Gravitational-firefly Algorithmmentioning

confidence: 99%

Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System

et al. 2021

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The load frequency control (LFC) and tie-line power are the key deciding factors to evaluate the performance of a multiarea power system. In this paper, the performance analysis of a two-area power system is presented. This analysis is based on two performance metrics: LFC and tie-line power. The power system consists of a thermal plant generation system and a hydro plant generation system. The performance is evaluated by designing a proportional plus integral (PI) controller. The hybrid gravitational search with firefly algorithm (hGFA) has been devised to achieve proper tuning of the controller parameter. The designed algorithm involves integral time absolute error (ITAE) as an objective function. For two-area hydrothermal power systems, the load frequency and tie-line power are correlated with the system generation capacity and the load. Any deviation in the generation and in the load capacity causes variations in the load frequencies, as well as in the tie-line power. Variations from the nominal value may hamper the operation of the power system with adverse consequences. Hence, performance of the hydrothermal power system is analyzed using the simulations based on the step load change. To elucidate the efficacy of the hGFA, the performance is compared with some of the well-known optimization techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA) and the firefly algorithm (FA).

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Section: Hybrid Gravitational-firefly Algorithmmentioning

confidence: 99%

Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System

et al. 2021

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“…Ref. Zhu et al [ 16 ] compared twelve SI algorithms for an uninhabited combat air vehicle path-planning problem and their results show that the spider monkey optimization (SMO) algorithm performs better than others in discovering a safe path.…”

Section: Related Workmentioning

confidence: 99%

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Selvaraj

Choi

2021

Sensors

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Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

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“…This type of structure is typical in many biological systems, such as insect colonies, flocks of birds, and schools of fish. The synergy between the swarm members provides each of them with advantages that they could not achieve on their own, such as protection against predators and a more reliable supply of food [14,85].…”

Section: Developed Solutionmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Cited by 16 publications

References 37 publications

Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System

Hybrid Gravitational–Firefly Algorithm-Based Load Frequency Control for Hydrothermal Two-Area System

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

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