Abstract
Global optimization for nonlinear function is a challenging issue. In this paper, an improved monarch butterfly algorithm based on local search and differential evolution is proposed. Local search strategy is first embedded into original monarch butterfly optimization to enhance the searching capability. Then, differential evolution is incorporated with the aim of balancing the exploration and exploitation. To evaluate the performance of proposed algorithm, some widely-used benchmark functions are tested, and the experiment results show its significant superiority compared with other state-of-the-art methods. In addition, the proposed algorithm is applied to PID tuning and FIR filter design, the superiority of solving practical problems is verified.
Highlights
In many engineering fields, such as automatic control, communication network, signal processing etc, the complex nonlinear optimization is a universal issue
In order to evaluate the performance of proposed algorithm, some experiments are carrying out, including fourteen benchmark functions, PID tuning and fix-point FIR filter design
SGA, monarch butterfly optimization algorithm (MBO), BBO and differential evolution (DE) converge to the value that is very close to DE-LSMBOs, which means these algorithms have a well performance in Function 12, while PSO stucks in the local minimum
Summary
In many engineering fields, such as automatic control, communication network, signal processing etc, the complex nonlinear optimization is a universal issue. Ghetas et al.[19] modified MBO into monarch butterfly harmony search (MBHS) by using a harmony search strategy These two algorithms improve the performance of MBO, some issues are still exist, for instance, they are easy to trap into local optimal solution, which will lead to premature convergence, and the searching capabilities need to be strengthened as well. The main contributions of this paper are summarised as follows: (1) An improved monarch butterfly algorithm based on local search and differential evolution (DE-LSMBO) is proposed; (2) A detail study on the superior performance of proposed algorithm is presented, including the selection of parameters and the comparison with other state-of-theart methods; (3) The capabilities of proposed algorithm dealing with practical problems in PID tuning and FIR filter design are verified.
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