Introduction to an optimization algorithm based on the chemical reactions

Astudillo, Leslie; Melín, Patricia; Castillo, Oscar

doi:10.1016/j.ins.2014.08.043

Cited by 17 publications

(4 citation statements)

References 20 publications

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“…Rate equations algorithm was evaluated on a set of complex benchmark functions and compared with other optimization algorithms. Simulation results displayed how the algorithm was able to reach near to the optimal values for some functions, performing better than the previously stated models, but more tests are needed to compare against some other algorithms that are proved to seek good solutions for larger dimensions [50].…”

Section: Reaction Typesmentioning

confidence: 93%

Modeling and optimization of hydrogenation of CO2: Estimation of kinetic parameters via Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms

Najari

Gróf

Saeidi

et al. 2019

International Journal of Hydrogen Energy

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DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: Reaction Typesmentioning

confidence: 93%

Modeling and optimization of hydrogenation of CO2: Estimation of kinetic parameters via Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms

Najari

Gróf

Saeidi

et al. 2019

International Journal of Hydrogen Energy

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“…Apart from this, stochastic search based algorithms include physics based [22][23], social based [24],biology based [25], swarm behavior [26], chemical based [27], music based [28], plant based [7], mathematics based [29], sports based [30], water based [31] algorithms.…”

Section: A Motivation and Prior Work Figure 1taxonomy Of State-of-the...mentioning

confidence: 99%

Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

et al. 2022

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The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local optima. Aquila Optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutationoccur to generate a new population.The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

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“…A. Zadeh in 1965, on the basis of a theory of fuzzy sets, which differs from the traditional crisp sets, because the degree of belonging is considered. At present, the use of logic systems (FLS) has increased, as can be observed in [24][25][26][27][28][29][30]. The Interval Type 2 fuzzy set has a fuzzy membership function, the membership grade for each element of this set is a fuzzy set in [0, 1], as can be observed in [31][32][33][34][35].…”

Section: Fuzzy Logic Systemsmentioning

confidence: 99%

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

Peraza

Valdez

Castro

et al. 2018

Advances in Operations Research

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This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

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Introduction to an optimization algorithm based on the chemical reactions

Cited by 17 publications

References 20 publications

Modeling and optimization of hydrogenation of CO2: Estimation of kinetic parameters via Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms

Modeling and optimization of hydrogenation of CO2: Estimation of kinetic parameters via Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms

Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

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