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Enhanced artificial hummingbird algorithm for global optimization and engineering design problems

ADVANCES IN ENGINEERING SOFTWARE 2024年 194卷

作者： Bakir, Huseyin Dogus Univ Vocat Sch Dept Elect & Automat TR-34775 Istanbul Turkiye

The artificial hummingbird algorithm (AHA) is a recently introduced versatile metaheuristic optimizer that simulates flight patterns and intelligent foraging skills of hummingbirds. It has gained widespread recognition for its simplicity and adaptability to a wide range of optimization problems. However, the limited ability of the algorithm to establish the exploration-exploitation balance leads to getting stuck in local solution traps and premature convergence. To eliminate these drawbacks, this study introduces an enhanced artificial hummingbird algorithm (enAHA) based on a dynamic fitness-distance balance (dFDB) operator. dFDB offers the opportunity to precisely balance exploration and exploitation throughout the optimization process with its dynamically adjustable weight coefficient. The convergence rate of the developed enAHA is tested on CEC 2020 and CEC 2022 benchmark problems. The enAHA and the original AHA results are statistically analyzed with the Wilcoxon signed-rank test. As per Wilcoxon test results, the proposed enAHA outperforms the original AHA algorithm for 70 %, 50 %, and 70 % of the CEC 2020 problems in 30-, 50-, and 100-dimensional optimization, respectively. In the CEC 2022 test suite, the enAHA showed a success rate of 58.33 % and 91.66 % with 10- and 20-dimensions. Moreover, the optimization capacity of enAHA is compared with the 29 state-of-the-art optimizers using the Friedman-rank test. Accordingly, the proposed enAHA algorithm ranked 1st, while the original AHA ranked 9th among the 30 competing algorithms. Furthermore, the practicability of the enAHA is validated on three engineering design problems: i) single -diode solar cell (SDSC) parameter extraction, ii) double-diode solar cell (DDSC) parameter estimation, and iii) optimization of pressure vessel design. The developed method provided minimum RMSE values of 7.730064E-04 for the SDSC and 7.422194E-04 for the DDSC. The enAHA algorithm achieved the best cost with a value of 5885.332773

关键词： Enhanced artificial hummingbird algorithm metaheuristic algorithm design Solar PV parameter extraction Dynamic fitness-distance balance selection method

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Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 240卷

作者： Bakir, Huseyin Dogus Univ Vocat Sch Dept Elect & Automat TR-34775 Istanbul Turkiye

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://***/matlabcentral/filee xchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.

关键词： A novel enhanced artificial rabbits optimization algorithm metaheuristic algorithm design Roulette and dynamic fitness -distance balance selection methods Optimal power flow Renewable energy sources

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