Chaotic coyote algorithm applied to truss optimization problems

Pierezan, Juliano; Coelho, Leandro dos Santos; Mariani, Viviana Cocco; Segundo, Emerson Hochsteiner de Vasconcelos; Prayogo, Doddy

doi:10.1016/j.compstruc.2020.106353

Cited by 74 publications

(20 citation statements)

References 53 publications

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“…Many novel algorithms such as Fitness Dependent Optimizer, Coyote Optimization, Manta Ray Foraging, Student Psychology-Based Optimization, Slime Mould, Barnacles Mating Optimizer, Falcon Optimization etc., especially inspired by nature and based on the flawless properties of biological systems, have been developed. [4][5][6][7][8][9][10] These algorithms have yielded effective results in global optimization problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Bees evaluate the sources in the neighborhood and if this source is good, they keep it in their memory. The mathematical expression of the source search in the neighborhood is given in Equation (5).…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…The artificial intelligence approach is one of the important issues that have been studied and developed for a long time to overcome deficiencies of traditional techniques. Many novel algorithms such as Fitness Dependent Optimizer, Coyote Optimization, Manta Ray Foraging, Student Psychology‐Based Optimization, Slime Mould, Barnacles Mating Optimizer, Falcon Optimization etc., especially inspired by nature and based on the flawless properties of biological systems, have been developed 4‐10 . These algorithms have yielded effective results in global optimization problems.…”

Section: Literature Reviewmentioning

confidence: 99%

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The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Korkmaz

Akgüngör

2021

Concurrency and Computation

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The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination Algorithm (FPA), Artificial Bee Colony Algorithm (ABC), Crow Search Algorithm (CSA), Krill Herd Algorithm (KH), and the Butterfly Optimization Algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved. K E Y W O R D Sair transport passenger demand, artificial bee colony algorithm, butterfly optimization algorithm, crow search algorithm, flower pollination algorithm, krill herd algorithm INTRODUCTIONAir transport is one of the most preferred modes of transport by passengers in the world and in Turkey because it provides important benefits such as safety, speed, and comfort. Air transport, which has a very important place in terms of trade and tourism development, also contributes to the social development and economic growth of countries.Air transport has shown a rapid development worldwide after the Second World War. The most important reasons for this progress are the development of aircraft production technology in the war years, the desire of the pilots to transform their skills to a commercial profit, and the desire of countries to undertake development ventures by rapidly erasing the negative traces of the war. For all these reasons, passenger and freight

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Section: Literature Reviewmentioning

confidence: 99%

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Korkmaz

Akgüngör

2021

Concurrency and Computation

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“…Compared with random search, chaotic sequence searches the search space thoroughly with higher probability, which enables the algorithm to go beyond the local optimum and maintain the diversity of the population. Based on the above analysis, to obtain a good initial solution position and speed up the convergence of the population, seven common chaotic mappings Sinusoidal, Tent, Kent, Cubic, Logistic, Gauss, and Circle were selected [62][63][64][65][66][67][68][69] and used to initialize the population of HHO algorithm. The results were analyzed and the optimal one for the HHO algorithm selected as the population initialization method for the improved algorithm.…”

Section: Chaotic Mappingmentioning

confidence: 99%

“…In order to verify the effectiveness of the CSHHO algorithm against the emerging swarm intelligence optimization algorithms in recent years. In this subsection, the CSHHO algorithm is compared with recently published meta-heuristics, including HHO [24], WOA [24], SCA [65] and CSO [66] to calculate the average precision mean and stability Std of each algorithm. The performance of CSHHO was tested against other optimization algorithms using a nonparametric test: the Bonferroni-Holm corrected Wilcoxon signed rank test.…”

Section: Influence Of Seven Common Chaotic Mappings On Hho Algorithmmentioning

confidence: 99%

Harris Hawks Optimization with Multi-Strategy Search and Application

et al. 2021

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The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.

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Text sentiment analysis on E‐shopping product reviews using chaotic coyote optimized deep belief network approach

Mohana¹,

Rajathi

Kousalya

et al. 2022

Concurrency and Computation

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Text sentiment analysis is mainly used to the customers benefits. In the existing works, the text sentiment analysis faces more troubles such as, disambiguation (removing unwanted terms), discussions, contrast, intensity, and excessive flections and complex sound structure with less accuracy. In this article, the text sentiment analysis on E‐shopping product using chaotic coyote optimized deep belief network approach is proposed to minimize the troubles in the sentiment analysis and increase the accuracy. The other name of sentiment analysis is subjective analysis. The main objective of this article is “classify the text sentiments according to the polarity (positive and negative) and to increase the accuracy.” Here, the E‐shopping Kaggle datasets are preprocessed and the features are extracted. Then, the extracted features of the trained data's are given using deep belief network (DBN) classifier to get pure sentiments (positive or negative) with accuracy. Here, the performance metrics of the accuracy, recall, and precision, F‐measure, specificity, and sensitivity are calculated. The simulation process is executed in Python platform. The proposed chaotic coyote optimized deep belief network (CCO‐DBN) attains accuracy 9.8%, precision 17.2%, recall 5.61%, F‐measure 17.07%, specificity 2.247%, sensitivity 13.25% is higher than the existing methods such as FCM‐DFA, GA, SVM‐RFA.

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Chaotic coyote algorithm applied to truss optimization problems

Cited by 74 publications

References 53 publications

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Harris Hawks Optimization with Multi-Strategy Search and Application

Text sentiment analysis on E‐shopping product reviews using chaotic coyote optimized deep belief network approach

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