Chaotic lightning search algorithm

Ouertani, Mohamed Wajdi; Manita, Ghaith; Korbaa, Ouajdi

doi:10.1007/s00500-020-05273-0

Cited by 18 publications

(5 citation statements)

References 41 publications

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“…The study of the type of chaotic function that is capable of improving an algorithm is very specific. Next, the algorithm and the associated chaotic function are highlighted: (i) Chaotic Lightning Search Algorithm (CLSA) (Ouertani et al, 2020)—Sine or Singer; (ii) Chaotic Dragonfly Algorithm (CDA) (Sayed et al, 2019)—Gauss; (iii) Chaotic Cuckoo Search (CCS) (Wang et al, 2016)—Sinusoidal; (iv) Chaotic Genetic Algorithm (CGA) (Kromer et al, 2013)—Logistic; and (v) Chaotic Fruit Fly Optimization Algorithm (CFOA) (Mitić et al, 2015)—Chebyshev.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Many algorithms have been considerably improved using these theories. Some examples using chaos theory are (i) chaotic bacterial foraging optimization (CBFO) (Chen et al, 2020); (ii) chaotic lightning search algorithm (CLSA) (Ouertani et al, 2020); (iii) chaotic dragonfly algorithm (CDA) (Sayed et al, 2019); (vi) chaotic grey wolf optimization (CGWO) (Yu et al, 2016); (v) chaotic cuckoo search (CCS) (; Wang et al, 2016); (vi) chaotic biogeography‐based optimization (CBBO) (Saremi et al, 2014); (vii) chaotic particle swarm optimization (CPSO) (Alatas et al, 2009); (viii) chaotic genetic algorithm (CGA) (Kromer et al, 2013); and so on. Likewise, other algorithms were improved using levy distribution: (i) LF cuckoo search (LFCS) (Rehman et al, 2016); (ii) LF bat algorithm (LFBA) (Xie et al, 2013); (iii) LF particle swarm optimization (LFPSO) (Richer & Blackwell, 2006); (iv) LF ant colony optimization (LFACO) (Liu & Cao, 2020); (v) LF grey wolf optimizer (LFGWO) (Amirsadri et al, 2017); and so on.…”

Section: Sunflower Optimization For Feature Selectionmentioning

confidence: 99%

“…MH, as stochastic techniques, have characteristics of randomness by generating a random number between 0 and 1 (rand). In general, it is generated by a uniform or Gaussian distribution, which can affect the efficiency of the MH and significantly transform its exploration and exploitation capabilities (Ouertani et al, 2020). Some alternative techniques can be considered to replace this rand number to upgrade the performance of MH in both precision and computational cost.…”

Section: Introductionmentioning

confidence: 99%

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A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

Pereira

Francisco

et al. 2023

Expert Systems

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Feature selection is a knowledge discovery tool to understand the problem by analysing features. In particular, the application of feature selection in data mining can not only improve the quality of extracted patterns and knowledge but also decrease computational costs. Various techniques have been applied to this complex optimization problem, in which metaheuristics have been validated to be superior. This study introduces a new metaheuristic known for having lean and fast programming, inspired by the sunflower's motions for feature selection for the first time. It is equipped with a v‐shaped transfer function and associated with the KNN classifier to become the binary sunflower optimization (BSFO). A total of 12 variants of BSFO are designed based on the chaos theory and Lévy flights, called improved binary sunflower optimization (IBSFO). A discussion between these improvement theories for feature selection has also not been made yet, and it is performed in this paper using 15 benchmark datasets from the UCI repository. The experimental results show that all variants can advance the fitness value of BSFO, and nine of them considerably decrease the computational costs. Furthermore, the chaotic BSFO with the Chebyshev function, taking replacement to normal rand, has the lowest fitness value (−11.37%) and execution time (−9.31%) than the original BSFO. Further, IBSFO is compared with another eight metaheuristics and outperforms these competitors on average fitness value and execution time. Overall, IBSFO proved to find subsets with reduced dimension and high accuracy with meagre computational cost due to its robust explorative and exploitative capacities.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Sunflower Optimization For Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

Pereira

Francisco

et al. 2023

Expert Systems

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“…The more uniform the initial particle distribution, the richer the diversity of the group, and the faster the optimal solution can be obtained. Chaos (Demir et al, 2020;Lian et al, 2020;Lu et al, 2020;Wu et al, 2020;Guo et al, 2021;Ouertani et al, 2021) refers to a nonlinear motion that can traverse all situations within a specified range. A chaotic sequence can represent all states in a prescribed space, which is commonly generated by mapping.…”

Section: Pso With Chaosmentioning

confidence: 99%

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Cheng

Zheng³

et al. 2021

Front. Neurorobot.

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The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

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“…Ouertani et al attempted to improve lightning search algorithm (LSA) and proposed a new CLSA. Eleven chaotic maps were utilized in the development of the CLSA algorithm while 7 benchmark functions were used for its performance measurement [61]. Xu and Mei proposed an advanced DC-WCA algorithm to help water cycle algorithm (WCA) escape from local optima and discover global optimal solutions.…”

Section: Physics-based Algorithms and Chaosmentioning

confidence: 99%

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Atali¹,

Pehlivan²,

Gürevin³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area -besides engineering systems-where chaos theory is widely employed is optimization problems.Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already in process.In this article, scientific studies that cover the most popular metaheuristic algorithms in the literature in recent years and chaos theory subtopics together are examined. Great number of studies on metaheuristic algorithms and chaos issues exist in scientific literature. This article, hitherto, had to be limited to the most common meta-heuristic algorithms. In chaos-based metaheuristic algorithms, some advantages such as easy implementation, short application time and search acceleration have been addressed.This article is believed to contribute to two groups of researchers: The first group includes researchers already using meta-heuristic algorithms and who will come to understand that they can even improve their current techniques with chaos theory subarguments. The second group includes those who currently utilize chaotic analysis and methods in areas like nonlinear prediction modeling design and who will realize that they can make their existing methods even smarter with metaheuristic methods. Some metaheuristic algorithms use chaotic maps to solve problems such as trapping in local optimal solutions and premature convergence. In this study, such algorithms are examined through the benchmark functions they use. In addition, PSO, FA, ABC, WO algorithms are compared in terms of their common features, and algorithms with the best success rate are presented.

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Chaotic lightning search algorithm

Cited by 18 publications

References 41 publications

A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

A comparison between chaos theory and Lévy flights in sunflower optimization for feature selection

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Chaos in metaheuristic based artificial intelligence algorithms: a short review

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