A chaotic optimization method based on logistic-sine map for numerical function optimization

Demir, Fahrettin Burak; Tuncer, Türker; Kocamaz, Adnan Fatih

doi:10.1007/s00521-020-04815-9

Cited by 72 publications

(31 citation statements)

References 37 publications

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“…Of the studies with PSO algorithm on Table 1: the study by Demir et al [33] using logistic-sine map observes the highest success rate result, in comparison with Rosenbrock benchmark function; the studies by Duarte and Carvalho [26] using llogistic map, Chen et al [27] using sin map, Demir et al [33] using logistic-sine map and Koyuncu [30] using Gauss map observe the highest success rate results, in comparison with Rastrigin benchmark function; the studies by Duarte and Carvalho [26] using logistic map, Chen et al [27] using sin map and Koyuncu [30] using Gauss map observe the highest success rate results, in comparison with Griewank benchmark function.…”

Section: Resultsmentioning

confidence: 99%

“…Logistic-sine map has good statistical result, and this advantage was used directly to calculate global optimum value in their study. In order to test the success rate of the proposed hybrid chaotic map-based optimization method, 16 benchmark functions were used [33]. The columns titled "Rosenbrock", "Rastrigin" and "Griewank" on the Tables 1-4 are the benchmark functions used for comparison in the studies listed below them.…”

Section: Swarm-based Algorithms and Chaosmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Swarm-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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“…A. Logistic-sine map The logistic-sine chaotic map (Demir, F. B et al, 2020) uses the concept of both logistic chaotic map and sine chaotic map. So it is also called a hybrid chaotic map.…”

Section: Methods and Modelsmentioning

confidence: 99%

Session Key based Image Cryptographic Algorithm using Logistic-Sine Map and Crossover Operator for IoT

Gupta¹,

Gupta²,

Shukla³

2021

JSR

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Due to the increasing demand for IoT applications in various fields such as healthcare, smart city, smart grids, industrial internet, etc. The privacy and security become a major issue in front of various researchers working in this field. This work proposed a novel image encryption algorithm based on a logisticsine map and crossover operator of a genetic algorithm. Various 1-D chaotic maps are discussed in the literature review, but in some cases, hybrid 1-D chaotic maps have higher performance than simple 1-D chaotic maps. So 1-D chaotic map along with a crossover operator is used in this work. Here logistic-sine maps and crossover are used to generate the random session key for each image encryption. Also, a crossover operator is used in encryption rounds for increasing confusion and diffusion. Here in this work, for each image encryption, a new session session key is generated. The proposed algorithm is tested on various parameters for effective randomness. Experimental results show that the proposed algorithm performance is better than existing algorithms in terms of randomness and secure enough to resist all the existing cryptanalytic attacks.

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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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A chaotic optimization method based on logistic-sine map for numerical function optimization

Cited by 72 publications

References 37 publications

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Session Key based Image Cryptographic Algorithm using Logistic-Sine Map and Crossover Operator for IoT

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

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