Chaotic League Championship Algorithms

Bingol, Harun; Alataş, Bilal

doi:10.1007/s13369-016-2200-9

Cited by 40 publications

(24 citation statements)

References 23 publications

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“…Update the position of the current search agent end if1 end for Check if any search agent goes beyond the search space and amend it Calculate the fitness of each search agent Update X * if there is a better solution t = t + 1 end while return X * Algorithm 2. The pseudo-code of the league champion optimization algorithm (LCA) [54] Initialize the league size (L) and the number of seasons (S); t = 1; Generate a league schedule; Initialize team formations (generate a population of L solutions) and determine the playing strengths (function or fitness value) along with them. Let the initialization also be the team's current best formation; While t < = S × (L − 1)…”

Section: Hybrid Metaheuristic Algorithmsmentioning

confidence: 99%

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

2019

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This study outlines the applicability of four metaheuristic algorithms, namely, whale optimization algorithm (WOA), league champion optimization (LCA), moth–flame optimization (MFO), and ant colony optimization (ACO), for performance improvement of an artificial neural network (ANN) in analyzing the bearing capacity of footings settled on two-layered soils. To this end, the models estimate the stability/failure of the system by taking into consideration soil key factors. The complexity of each network is optimized through a sensitivity analysis process. The performance of the ensembles is compared with a typical ANN to evaluate the efficiency of the applied optimizers. It was shown that the incorporation of the WOA, LCA, MFO, and ACO algorithms resulted in 14.49%, 13.41%, 18.30%, and 35.75% reductions in the prediction error of the ANN, respectively. Moreover, a ranking system is developed to compare the efficiency of the used models. The results revealed that the ACO–ANN performs most accurately, followed by the MFO–ANN, WOA–ANN, and LCA–ANN. Lastly, the outcomes demonstrated that the ACO–ANN can be a promising alternative to traditional methods used for analyzing the bearing capacity of two-layered soils.

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Section: Hybrid Metaheuristic Algorithmsmentioning

confidence: 99%

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

2019

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“…LCA uses the rules in competition of sport teams. Game power is aimed to be increased by using the team formation, which is performed by each team [2,6]. Each team updates its formation and game style in order to win matches in next weeks by examining the previous matches.…”

Section: League Championship Algorithmmentioning

confidence: 99%

“…Each team updates its formation and game style in order to win matches in next weeks by examining the previous matches. Championship continues until the end of the season [2,6,7].…”

Section: League Championship Algorithmmentioning

confidence: 99%

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Investigating Performance of Intelligent Sine Cosine Algorithm Within Unconstrained Unimodal and Multimodal Search and Optimization Problems

Alatas¹

2017

ijipsa

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Classical optimization algorithms are insufficient to solve non-linear problems. Even if it is an existing solution, it takes too much time to use it. Therefore, intelligent metaheuristic algorithms are used. General purposed intelligent metaheuristic techniques are evaluated in ten groups: Plant-based, biology-based, social-based, music-based, physicsbased, chemical-based, sports-based, mathematics-based, water-based, swarm-based, and hybrid methods. Sine Cosine Algorithm (SCA) is a mathematics based intelligent optimization algorithm that simulates a based on sine and cosine functions for general optimization. League Championship Algorithm (LCA) is a sports based intelligent optimization algorithm and simulates a championship for general optimization with artificial league and artificial teams for several weeks. In this article, LCA and SCA have been compared under equal conditions. Comparative analysis of these intelligent algorithms have been examined in complex benchmark functions. According to the simulation results, SCA has better performance within multi-modal functions than LCA while LCA has better results within unimodal functions under the same conditions according to the selected problem and algorithm parameters.

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“…[27]. Son yıllarda bu bilinçle araştırmacılar, yeni metasezgisel yöntemleri başarılı bir şekilde literatüre kazandırmışlar ve başarılı uygulamalar gerçekleştirmişlerdir [28].…”

Section: Gi̇ri̇ş (Introduction)unclassified

Genelleşti̇ri̇lmi̇ş İlgi̇nç Siniflandirma Kurallarinin Yapay Ki̇myasal Reaksi̇yon Opti̇mi̇zasyon Algori̇tmasi İle Keşfi̇

Alataş¹,

Özer²

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Classification rules mining is one of the most studied data mining problems. In this article, chemistry based Artificial Chemical Reaction Optimization Algorithm (ACROA) has been for the first time used for mining of generalized classification rules, which is a complex and no well researched generalized variant of classification rules mining where there is more than one goal attribute to be predicted. Furthermore, interestingness measure has been added by performing the adaptations to the algorithm in order to make the rules mined by the algorithm not only accurate and comprehensible but also interesting, surprising, and unexpected. Different rule sets within different databases satisfying different objectives have been flexibly mined by adapting the representation scheme and objective function. Performance of ACROA in classification rules mining within different real public databases have been compared that of genetic algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm. It has shown that performance of ACROA within this special task of data mining is promising. ACROA can be an efficient solution method for different data mining tasks such as association rules mining, clustering rules mining, sequential pattern mining, and etc.

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Chaotic League Championship Algorithms

Cited by 40 publications

References 23 publications

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

Neural Computing Improvement Using Four Metaheuristic Optimizers in Bearing Capacity Analysis of Footings Settled on Two-Layer Soils

Investigating Performance of Intelligent Sine Cosine Algorithm Within Unconstrained Unimodal and Multimodal Search and Optimization Problems

Genelleşti̇ri̇lmi̇ş İlgi̇nç Siniflandirma Kurallarinin Yapay Ki̇myasal Reaksi̇yon Opti̇mi̇zasyon Algori̇tmasi İle Keşfi̇

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