<![CDATA[The Dung Beetle Optimization algorithm (DBO) is a swarm-based intelligence algorithm with competitive performance against other popular optimization algorithms. However, its process is often fallen in local optimum, insufficient accuracy, and slow convergence speed due to a lack of combination or collaboration between search agents. This research proposes an advanced DBO approach combining the Harris Hawks Optimizer (HHO) and Nelder Mead method to improve slow convergence speed, insufficient accuracy, and premature convergence. The Nelder Mead method is used in the subpopulation of ball-rolling to reduce the probability of falling in local optimum, along with “seven kills” strategy of HHO method that is combined in the former iterations of the DBO algorithm to enhance its global search capacity and convergence speed. The performance of the proposed enhanced dung beetle optimization (EDBO) algorithm is evaluated via 30 CEC-2017 benchmark functions and compared with several representative meta-heuristic algorithms, including the original DBO and HHO, as well as three recently proposed methods: RUN, SMA, and AO. The result shows that EDBO consistently achieves superior performance over most of the C-test functions in terms of solution quality and robustness. Additionally, when applied to the optimization of the operating cost of a solar-connected residential power system, the proposed EDBO attains the best or highly competitive global optimum compared with the competing algorithms.]]>
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