Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

On Selection of a Benchmark by Determining the Algorithms' Qualities

ABSTRACT: The authors got the motivation for writing the article based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Objective Optimization benchmarks that were issued several times in the last decade, serve as a testbed for evaluating the collection of nature-inspired algorithms selected in our study. Indeed, this article addresses two research questions: (1) How the selected benchmark affects the ranking of the particular algorithm, and (2) If it is possible to find the best algorithm capable of outperforming all the others on all the selected benchmarks. Ten outstanding algorithms (also winners of particular competitions) from different periods in the last decade were collected and applied to benchmarks issued during the same time period. A comparative analysis showed that there is a strong correlation between the rankings of the algorithms and the benchmarks used, although some deviations arose in ranking the best algorithms. The possible reasons for these deviations were exposed and commented on.This work was supported in part by the Slovenian Research Agency (Projects J2-1731 and L7-9421) under Grant P2-0041, in part by the Project PDE-GIR of the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie under Grant 778035, and in part by the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) of the Agencia Estatal de Investigacion and European Funds EFRD (AEI/FEDER, UE) under Grant TIN2017–89275-R

Diversifying search in bee algorithms for numerical optimisation

© Springer Nature Switzerland AG 2018. Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks