Fuzzy Logic Controller Design for Tuning the Cooperation of Biology-Inspired Algorithms

Akhmedova, Shakhnaz; Semenkin, ‪Eugene; Stanovov, Vladimir; Vishnevskaya, Sophia

doi:10.1007/978-3-319-61833-3_28

Cited by 5 publications

(11 citation statements)

References 11 publications

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“…The described problem was solved by controllers based on fuzzy logic [28]. The fuzzy controller implements a more flexible parameter tuning algorithm, compared to the original approach used in COBRA [29]. The fuzzy controller operates by using special fuzzification, inference and defuzzification schemes [28], which allow generating real-valued outputs.…”

Section: Fuzzy-controlled Cobramentioning

confidence: 99%

“…The fuzzy controller operates by using special fuzzification, inference and defuzzification schemes [28], which allow generating real-valued outputs. In the mentioned study [29], component algorithms are rated with success values, which were used as the fuzzy-controller inputs, and the amount of population size changes as its outputs.…”

Section: Fuzzy-controlled Cobramentioning

confidence: 99%

“…The success of every component was determined as the best achieved fitness value of the corresponding component. This choice was made in accordance with the research presented in [29]. The 7-th input variable was determined as the ratio of the number of steps, during which the best-found fitness value (found by all algorithms together) was improved, to the adaptation period, which was a parameter.…”

Section: Fuzzy-controlled Cobramentioning

confidence: 99%

“…Later, the fuzzy logic-based controllers [28] were proposed for the automated selection of biology-inspired algorithms to be included in the ensemble from a predefined set, and the number of individuals in each population [29]. The idea of using fuzzy controllers for parameter adaptation of the heuristic was previously explored by researchers, for instance in [30,31], and their usefulness was established.…”

Section: Introductionmentioning

confidence: 99%

“…The COBRA-f approach, like the original COBRA algorithm, was developed for continuous optimization [29], but despite its effectiveness compared to the mentioned biology-inspired algorithms (in other words its components), the COBRA-f meta-heuristic still needs to address the problem of exploitation and exploration [33]. As was noted before, a variety of ideas has been proposed to find the exploration-exploitation balance in the population-based biology-related algorithms, including methods of parameter adaptation [34][35][36], island models [37,38], population size control [39,40], and many others.…”

Section: Introductionmentioning

confidence: 99%

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Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

et al. 2020

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In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperative work of six well-known biology-inspired algorithms, referred to as components. However, it was established that the search efficiency of COBRA depends on its ability to keep the exploitation and exploration balance when solving optimization problems. The new modification of the COBRA approach is based on other method for generating potential solutions. This method keeps a historical memory of successful positions found by individuals to lead them in different directions and therefore to improve the exploitation and exploration capabilities. The proposed technique was applied to the COBRA components and to its basic steps. The newly proposed meta-heuristic as well as other modifications of the COBRA approach and components were evaluated on three sets of various benchmark problems. The experimental results obtained by all algorithms with the same computational effort are presented and compared. It was concluded that the proposed modification outperformed other algorithms used in comparison. Therefore, its usefulness and workability were demonstrated.

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Section: Fuzzy-controlled Cobramentioning

confidence: 99%

Section: Fuzzy-controlled Cobramentioning

confidence: 99%

Section: Fuzzy-controlled Cobramentioning

confidence: 99%