Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

Mohamed, Ali Wagdy; Sallam, Karam M.; Agrawal, Prachi; Hadi, Anas A.; Mohamed, Ali Khater

doi:10.1007/s00521-022-07788-z

Cited by 27 publications

(13 citation statements)

References 49 publications

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“…This is an advantage, but it also implies the limitations of the proposal, because it is applicable to the characteristics of IKP for robots or animation applications where an initial posture and the final value (not the solution) of the optimization function are met, and the final and initial postures are similar. These characteristics contrast with the benchmark problems shown in [18,20,21], where the optimal value was assumed to be unknown; in some cases, the best value was not necessarily the optimal, and it was not required that the final solution has similarity with a specific initial design vector.…”

Section: Experiments and Resultsmentioning

confidence: 96%

Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics

et al. 2023

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The inverse kinematics problem (IKP) is fundamental in robotics, but it gets harder to solve as the complexity of the mechanisms increases. For that reason, several approaches have been applied to solve it, including metaheuristic algorithms. This work presents a proposal for solving the IKP of a doubly articulated kinematic chain by means of a modified differential evolution (DE) algorithm. The novelty of the proposal is both in the modeling of the problem and the modification to the DE for solving it. The modeling is inspired by a technique used in animation software to recreate movements by dividing the complete trajectory in a number of segments. Each segment represents a single optimization problem linked to the IKP as a sequence that is solved by the modified DE where the initial population for each single problem is biased by using the solution of the previous one. The approach produces solutions for positioning the end effector in a specific point within the work space while minimizing the angular displacement between the initial and final poses. The proposal was able to obtain solutions requiring a fewer total execution cycles compared to the usual approach of solving only one optimization problem related to the inverse kinematics. Different trajectories were used to test the solutions generated by the proposed approach, and the set of conditions that must be covered to apply it to solve the IKP of a particular mechanism are presented.

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Section: Experiments and Resultsmentioning

confidence: 96%

Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics

et al. 2023

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“…The IEEE CEC 2021 functions, which include 20 dimensions and evaluate the performance of algorithms on shifted, rotated, and biased functions, and the IEEE CEC 2017 test suite, which includes 50 dimensions, were used to thoroughly examine the performance of the LWSSA. Further details about the IEEE CEC 2017 and IEEE CEC 2021 functions can be found in [59,60], respectively. The results of the experiment were obtained through 30 independent runs, each comprising 2500 iterations, for statistical analysis.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…There are a total of 10 various functions included in the IEEE CEC 2021 benchmark suite. The associated technical paper [60] contains in-depth explanations of each and every one of these functions. The extra material includes an illustration that provides a summary of all 10 functions in total.…”

Section: Experiments 1: Benchmark Examination For the Ieee Cec 2021mentioning

confidence: 99%

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

Mohammed,

Hussein,

Haddani

et al. 2024

Mathematics

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The Salp Swarm Algorithm (SSA) is a bio-inspired metaheuristic optimization technique that mimics the collective behavior of Salp chains hunting for food in the ocean. While it demonstrates competitive performance on benchmark problems, the SSA faces challenges with slow convergence and getting trapped in local optima like many population-based algorithms. To address these limitations, this study proposes the locally weighted Salp Swarm Algorithm (LWSSA), which combines two mechanisms into the standard SSA framework. First, a locally weighted approach is introduced and integrated into the SSA to guide the search toward locally promising regions. This heuristic iteratively probes high-quality solutions in the neighborhood and refines the current position. Second, a mutation operator generates new positions for Salp followers to increase randomness throughout the search. In order to assess its effectiveness, the proposed approach was evaluated against the state-of-the-art metaheuristics using standard test functions from the IEEE CEC 2021 and IEEE CEC 2017 competitions. The methodology is also applied to a risk assessment of cardiovascular disease (CVD). Seven optimization strategies of the extreme gradient boosting (XGBoost) classifier are evaluated and compared to the proposed LWSSA-XGBoost model. The proposed LWSSA-XGBoost achieves superior prediction performance with 94% F1 score, 94% recall, 93% accuracy, and 93% area under the ROC curve in comparison with state-of-the-art competitors. Overall, the experimental results demonstrate that the LWSSA enhances SSA’s optimization ability and XGBoost predictive power in automated CVD risk assessment.

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“…The algorithm is based on MATLAB 2022b and implemented in the M language. The CEC2018 test suite consists of a total of 30 single-objective test functions, with search intervals ranging between [− 100, 100] 52 . All the test functions aim to solve the minimization problem, with D representing the dimension (30 dimensions), as follows:…”

Section: Simulation Comparison and Performance Testmentioning

confidence: 99%

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

Wang,

Cao,

Chen

et al. 2024

Sci Rep

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To address the issues of lacking ability, loss of population diversity, and tendency to fall into the local extreme value in the later stage of optimization searching, resulting in slow convergence and lack of exploration ability of the artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a gorilla search algorithm that integrates the positive cosine and Cauchy's variance (SCAGTO). Firstly, the population is initialized using the refractive reverse learning mechanism to increase species diversity. A positive cosine strategy and nonlinearly decreasing search and weight factors are introduced into the finder position update to coordinate the global and local optimization ability of the algorithm. The follower position is updated by introducing Cauchy variation to perturb the optimal solution, thereby improving the algorithm's ability to obtain the global optimal solution. The SCAGTO algorithm is evaluated using 30 classical test functions of Test Functions 2018 in terms of convergence speed, convergence accuracy, average absolute error, and other indexes, and two engineering design optimization problems, namely, the pressure vessel optimization design problem and the welded beam design problem, are introduced for verification. The experimental results demonstrate that the improved gorilla search algorithm significantly enhances convergence speed and optimization accuracy, and exhibits good robustness. The SCAGTO algorithm demonstrates certain solution advantages in optimizing the pressure vessel design problem and welded beam design problem, verifying the superior optimization ability and engineering practicality of the SCAGTO algorithm.

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Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

Cited by 27 publications

References 49 publications

Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics

Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics

Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization

Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications

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