Cooperative Model of Evolutionary Algorithms Applied to CEC 2019 Single Objective Numerical Optimization

Bujok, Petr; Zamuda, Aleš

doi:10.1109/cec.2019.8790317

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…The winner of the CEC 2022 competition EA4eig [7] is adapted from the ensemble of four population-based metaheruistics -CoBiDE [17], IDEbd [18], CMA-ES [19], and jSO [20] -proposed in [21] by extending the application range of the Eigen approach from CoBiDE to all the constituent algorithms. NL-SHADE-LBC [8] ranks second in the competition.…”

Section: Algorithms Under the New Settingmentioning

confidence: 99%

Ensemble of Population-Based Metaheuristics With Exchange of Individuals Chosen Based on Fitness and Lifetime for Real Parameter Single Objective Optimization

Zhu

et al. 2023

Preprint

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Real parameter single objective optimization has been studied for decades to find the best decision vector to minimize (or maximize) an objective multi-dimension function. In recent, a new setting is proposed and applied in this field based on the consideration that solving difficulty scales exponentially with the increase of dimensionality. Under the new setting, Differential Evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic - Gaining-Sharing Knowledge based algorithm (GSK) becomes a dark horse. Furthermore, ensemble of the above two types of algorithm - APGSK-IMODE - is proposed in literature. In this paper, we change the scheme for executing communication between the two constituent algorithms - APGSK and IMODE, and propose APGSK-IMODE-II. In our experiment, our algorithm is compared with state-of-the-art algorithms. Experimental results show that our APGSK-IMODE-II is very competitive.

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Section: Algorithms Under the New Settingmentioning

confidence: 99%

Ensemble of Population-Based Metaheuristics With Exchange of Individuals Chosen Based on Fitness and Lifetime for Real Parameter Single Objective Optimization

Zhu

et al. 2023

Preprint

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“…The last 5 functions in Table 1 are shifted functions, which are mainly to avoid the situation that some algorithms copy one parameter to another to generate a neighbor solution [ 53 ]. The other 10 test problems ( f 22 ∼ f 31 ) considered in this paper (see Table 2 ) regard composite benchmark functions considered in the IEEE CEC 2019 special session [ 54 ]. These benchmark functions are more complex than the first 21 benchmark functions, and f 22 ∼ f 31 are designed to have a minimum value of 1.…”

Section: Experiments and Analysismentioning

confidence: 99%

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Sun

Feng

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investigate the effectiveness of REGWO, it is evaluated on 21 widely used benchmark functions and IEEE CEC 2019 test functions. Experimental results show/ that REGWO performs better than the other competitors on most benchmarks.

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“…Firstly, the classical benchmark test functions encompass a collection of nineteen functions, which are classified into three distinct categories: unimodal, multimodal, and composite [13]. The second category involves ten functions, referred to as the CEC-C06 2019 conference functions [14], specifically employed to evaluate and adjust the efficacy of newly proposed algorithms. These functions continue to be extensively adjusted within modern benchmark collections.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Algorithm Selection through Comprehensive Performance Evaluation: Statistical Analysis of Stochastic Algorithms

Amin,

Aladdin,

Hasan

et al. 2023

Computation

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Analyzing stochastic algorithms for comprehensive performance and comparison across diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a wide spectrum of test functions, including both classical benchmarks and CEC-C06 2019 conference functions, distinct patterns of performance emerge. In specific situations, underscoring the importance of choosing algorithms contextually. Additionally, researchers have encountered a critical issue by employing a statistical model randomly to determine significance values without conducting other studies to select a specific model for evaluating performance outcomes. To address this concern, this study employs rigorous statistical testing to underscore substantial performance variations between pairs of algorithms, thereby emphasizing the pivotal role of statistical significance in comparative analysis. It also yields valuable insights into the suitability of algorithms for various optimization challenges, providing professionals with information to make informed decisions. This is achieved by pinpointing algorithm pairs with favorable statistical distributions, facilitating practical algorithm selection. The study encompasses multiple nonparametric statistical hypothesis models, such as the Wilcoxon rank-sum test, single-factor analysis, and two-factor ANOVA tests. This thorough evaluation enhances our grasp of algorithm performance across various evaluation criteria. Notably, the research addresses discrepancies in previous statistical test findings in algorithm comparisons, enhancing result reliability in the later research. The results proved that there are differences in significance results, as seen in examples like Leo versus the FDO, the DA versus the WOA, and so on. It highlights the need to tailor test models to specific scenarios, as p-value outcomes differ among various tests within the same algorithm pair.

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Cooperative Model of Evolutionary Algorithms Applied to CEC 2019 Single Objective Numerical Optimization

Cited by 9 publications

References 21 publications

Ensemble of Population-Based Metaheuristics With Exchange of Individuals Chosen Based on Fitness and Lifetime for Real Parameter Single Objective Optimization

Ensemble of Population-Based Metaheuristics With Exchange of Individuals Chosen Based on Fitness and Lifetime for Real Parameter Single Objective Optimization

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Enhancing Algorithm Selection through Comprehensive Performance Evaluation: Statistical Analysis of Stochastic Algorithms

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