Self-adaptive search equation-based artificial bee colony algorithm on the CEC 2014 benchmark functions

Yavuz, Gürcan; Aydın, Doğan; Stützle, Thomas

doi:10.1109/cec.2016.7743920

Cited by 8 publications

(10 citation statements)

References 31 publications

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“…We have used the default parameter values for SSEABC and CMAES algorithms which were given in [14] and [12] respectively. A maximum of 10 4 D function evaluations was used.…”

Section: Methodsmentioning

confidence: 99%

“…e local search procedure is called only when it is expected that its invocation will result in an improvement of the-best-so-far solution. In previous implementation of SSEABC [14], competitive local search selection procedure was used. However, for the BBOB testbed, we used CMA-ES algorithm [12] as the local search procedure because competitive local search selection provides a wasteful use of function evaluations.…”

mentioning

confidence: 99%

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Self-adaptive search equation-based artificial bee colony algorithm with CMA-ES on the noiseless BBOB testbed

Aydın

Yavuz

2017

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Self-Adaptive Search Equation based Arti cial Bee Colony (SSEABC) is a recent variant of Arti cial Bee Colony (ABC) algorithm. SSEABC proposed three enhancements on the canonical ABC algorithm. ese are the self-adaptive search equation selection strategy, hybridization with a local search procedure and incremental population size strategy. e performance of SSEABC is tested on CEC 2015 benchmark suite and ranked third within all participants of competition. In this paper, we benchmarks SSEABC using the noisefree BBOB function testbed. We also compare SSEABC performance to PSO, ABC and GA algorithms. CCS CONCEPTS •Computing methodologies → Continuous space search;

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“…We have used the default parameter values for SSEABC and CMAES algorithms which were given in [14] and [12] respectively. A maximum of 10 4 D function evaluations was used.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Self-adaptive search equation-based artificial bee colony algorithm with CMA-ES on the noiseless BBOB testbed

Aydın

Yavuz

2017

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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“…The SSEABC algorithm [28] introduces three strategies to the basic ABC algorithm to improve performance quality. The first strategy is a self-adaptive search equation determination, the second is incrementing the size of population during the execution, and the third one is the competitive local search selection strategy.…”

Section: Self-adaptive Search-equation-based Artificial Bee Colony Almentioning

confidence: 99%

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Durmuş¹,

Yavuz²,

Aydın³

2019

Turk J Elec Eng & Comp Sci

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Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.

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“…The members of the EAs family base on the DE algorithm widened from its original version throughout the self-adaptive versions jDE [12] and SaDE [13] to the SHADE [14] and its improved variants LSHADE [15], iL-SHADE [16], jSO [17] and LSHADE_RSP [18]. The members of the SI-based family include the original Artificial Bee Colony (ABC) algorithm [19] and its self-adaptive variant SSEABC [20]. All algorithms were applied to the CEC'13 [9], CEC'14/'16 [9], and CEC'17/'18 [11] benchmark function suites.…”

Section: Introductionmentioning

confidence: 99%

On Selection of a Benchmark by Determining the Algorithms’ Qualities

et al. 2021

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The authors got the motivation for writing the paper 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 paper 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.

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Self-adaptive search equation-based artificial bee colony algorithm on the CEC 2014 benchmark functions

Cited by 8 publications

References 31 publications

Self-adaptive search equation-based artificial bee colony algorithm with CMA-ES on the noiseless BBOB testbed

Self-adaptive search equation-based artificial bee colony algorithm with CMA-ES on the noiseless BBOB testbed

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

On Selection of a Benchmark by Determining the Algorithms’ Qualities

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