Tuning maturity model of ecogeography-based optimization on CEC 2015 single-objective optimization test problems

Zheng, Yujun; Wu, Xiaobei

doi:10.1109/cec.2015.7257001

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Step ¼0:35 þ 0:15 cos T 2 p FEs MaxFEs : ð16Þ The performance of the algorithm was evaluated based on three benchmarks CEC 2013 (Liang et al 2013), CEC 2015 (Liang et al 2014), and CEC 2017 (Awad et al 2016) and compared to the state-of-the-art algorithms as SMASE (Caraffini et al 2013b), CMAES-RIS (Caraffini et al 2013a), DE-APC (Elsayed et al 2013), fk-PSO (Nepomuceno and Engelbrecht 2013) (CEC 2013); TEBO (Zheng and Wu 2015), ICMLSP (Chen et al 2015), SaDPSO (Liang et al 2015), dynFWACM (Yu et al 2015b) (CEC 2015); SOMA All-ToOne and AllToAll, SOMAT3A (Diep 2019), RB-IPOP-CMA-ES (Biedrzycki 2015), PPSO (Tangherloni et al 2017), TLBO-FL (Kommadath and Kotecha 2017), SAMPE-Jaya (Pandey 2016) (CEC 2017). The results show that the principle of leader and migrants selection and control parameters adaptation significantly improved the performance of the SOMA.…”

Section: Control Parameters Pools and Adaptationmentioning

confidence: 99%

Self-organizing migrating algorithm: review, improvements and comparison

Skanderová

2022

Artif Intell Rev

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The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms.

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Section: Control Parameters Pools and Adaptationmentioning

confidence: 99%

Self-organizing migrating algorithm: review, improvements and comparison

Skanderová

2022

Artif Intell Rev

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“…We compared sdABC with some excellent meta-heuristic algorithms which had taken part in the competition on real-parameter single objective optimization at CEC2015, including TEBO [67], dynFWACM [68], SaDPSO [69], ABC-X-LS [70], MVMO [71], and DEsPA [72]. The results of all these algorithms are directly collected from the original papers, and downloaded from the homepage of Prof. P.N.…”

Section: Comparison With Meta-heuristic Algorithms In Cec2015mentioning

confidence: 99%

Self-adaptive differential artificial bee colony algorithm for global optimization problems

Tianfield

2019

Swarm and Evolutionary Computation

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Artificial bee colony algorithm (ABC) has attracted wide attention in the recent decade. Although ABC algorithms can achieve good performance on separable problems by optimizing each variable independently, their performances on complex non-separable problems are still unsatisfactory. In this paper, through incorporating multiple differential search strategies and a self-adaptive mechanism within the framework of ABC, we propose a new ABC algorithm, called self-adaptive differential artificial bee colony (sdABC) algorithm. By means of differential search strategies, more variables will be updated each time based on the combination of mutation and crossover. Thus, sdABC has much enhanced ability for solving complex non-separable problems. Our proposed sdABC algorithm is evaluated on 28 benchmarks functions, including both common separable problems and complex non-separable CEC2015 functions. The experimental results show that sdABC can achieve much more desirable results compared with previous ABC algorithms on both separable and non-separable functions. Furthermore, the performance of our sdABC is also very competitive compared with well-established differential evolution and other meta-heuristic algorithms.

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