Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems

Aydilek, İbrahim Berkan; Karaçizmeli, İzzettin Hakan; Tenekeci, Mehmet Emin; Kaya, Serkan; Gümüşçü, Abdülkadir

doi:10.1007/s12046-021-01572-w

Cited by 13 publications

(7 citation statements)

References 46 publications

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“…Aiming at the different kinds of COVID-19 CT images, diversity-enhanced hybrid firefly algorithm (DeHFA) [54] , uniform orthogonal firefly algorithm (UOFA) [55] , chaos optimization firefly algorithm (COFA) [44] , firefly algorithm based on the random mechanism and exponentially decreasing inertia weight (FARMEDI,paper method) are used for simulation comparison tests of segmentation parameters optimization. Moreover, aiming at the early COVID-19 CT image, we use the meta-heuristic algorithms to optimize image segmentation, including grasshopper optimization algorithm (GOA) [56] , equilibrium optimization algorithm (EOA) [57] , [58] , marine predators algorithm (MPA) [59] , [60] and traditional firefly algorithm (TFA).…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Guo-wei

Guo

Han

et al. 2022

Biomedical Signal Processing and Control

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Guo-wei

Guo

Han

et al. 2022

Biomedical Signal Processing and Control

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“…Because of qualities such as high error tolerance, automated segmentation of the population into subgroups, and non-sensitivity to first values, this method is extensively used for solving optimization and engineering problems, and it can produce good results [30]. For simplicity, this algorithm is based on the following three characteristics: Fireflies are unisex so that any individual will be attracted to others regardless of their gender; their attractiveness is proportionate to their brightness, so when there are two lighting fireflies, the less brilliant one will migrate towards the brighter one; and the brightness of a firefly is associated or determined by the objective function [31,32]. The standard FA consists of two important characteristics; the first one is the formulation of light intensity change Ȋ that will be computed using Eq.…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

“…( 18). suggested hybrid combination between the FA and the PSO will find a better solution for firefly's local search capabilities and the PSO algorithm's global search capabilities [30,32]. The ideas of personal best and global best are introduced into the FA in the proposed algorithm.…”

Section: The Proposed Hybrid Optimization Algorithmmentioning

confidence: 99%

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Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

2022

IJIES

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The primary goal of a mobile robot is to reach the desired goal by traversing an optimized path defined according to some criteria such as time, distance, and safety of the robot from any obstacles that may be in its way. Therefore, the backbone of autonomous mobile robots (AMRs) is path planning and avoiding obstacles. Many algorithms for path planning and obstacle avoidance have been presented by many researchers and each of these algorithms has several benefits and drawbacks. This paper focuses on comparing the performance of several metaheuristic algorithms that result in a more efficient, smoother, and shorter path for the mobile robot to reach the target in a complex environment. These algorithms include particle swarm optimization (PSO), chaotic particle swarm optimization (CPSO), modified chaotic particle swarm optimization (MCPSO), and firefly algorithm (FA). On the other hand, the paper proposes a hybrid algorithm by combining the FA and the MCPSO, namely the (HFAMCPSO). To demonstrate the effectiveness of the proposed algorithm in terms of the optimum cost function and obtaining the shortest path length, the optimal solution is compared to those of other path planning algorithms. Moreover, inverse dynamic and kinematic modeling are utilized to obtain the best torque and the best velocity actions for the wheels of the autonomous mobile robot. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equals (43.3%) and (25.5%) compared to the radial cell decomposition (RCD) and the A* algorithm, respectively. Moreover, the enhancement on the path length equals (2.3%) and (22.7%) compared to the firefly algorithm (FA) and the genetic algorithm (GA), respectively. All methods are simulated in an environment with static obstacles using the 2018b MATLAB package.

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“…Chaos theory is widely used in swarm intelligence algorithms due to its randomness and non-repetition. Compared with random search, chaos theory can make full use of the search space, so it is often used to enhance the diversity of the initial population and improve the algorithm to achieve optimized performance [ 17 , 18 , 19 , 20 ]. Circle Map maps the variables to the value interval of the chaotic variable space and finally transforms the solution linearly into the optimization variable space.…”

Section: Chaos Mutation Adaptive Sparrow Search Algorithmmentioning

confidence: 99%

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Qiu¹,

Li²

2022

Sensors

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In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.

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Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems

Cited by 13 publications

References 46 publications

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image

Intelligent Hybrid Path Planning Algorithms for Autonomous Mobile Robots

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

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