Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems

doi:10.1007/s10586-024-04586-1

Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems

2024-06-13

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DOI：10.1007/s10586-024-04586-1

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This paper proposes a new intelligent optimization algorithm named Black Eagle Optimizer (BEO) based on the biological behaviour of the black eagle. The BEO algorithm combines the biological laws of the black eagle and mathematical transformations to guide the search behaviour of the particles. The highly adaptive BEO algorithm has strong optimisation capabilities due to its unique algorithmic structure and novel iterative approach. In the performance testing experiments of the BEO algorithm, this paper firstly conducts the parametric analysis experiments of the BEO algorithm, then analyses the complexity of the BEO algorithm, and finally conducts a comprehensive testing of the performance of the BEO algorithm on 30 CEC2017 test functions with the widest variety of functions and 12 newest CEC2022 test functions, and its performance is compared with the seven state-of-the-art optimization algorithms. The test results show that the convergence accuracy of the BEO algorithm reaches the theoretical value in 100% of unimodal functions, the convergence accuracy is higher than the comparison algorithm in 78.95% of complex functions, and the standard deviation ranks in the top three in 90.48% of functions, which demonstrates the outstanding local optimisation ability, global optimisation ability and stability of BEO algorithm. Meanwhile, the BEO algorithm also maintains a fast convergence speed. However, the complexity analysis shows that the BEO algorithm has the disadvantage of slightly higher complexity. In order to verify the optimisation ability of the BEO algorithm in real engineering problems, we used the BEO algorithm to deal with four complex engineering design problems. The experimental results show that the BEO algorithm has excellent convergence accuracy and stability when dealing with real engineering problems, but the real-time performance is slightly below average. Therefore, the BEO algorithm is optimal for handling non-real-time engineering optimisation problems. The source code of the BEO algorithm is available at https://github.com/haobinzhang123/A-metaheuristic-algorithm.

Keyword:

Black eagle optimizer

Convergence accuracy

Convergence speed

Stability

Engineering problems

Black eagle optimizer: a metaheuristic optimization method for solving engineering optimization problems

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