Abstract
The effective selection of Variable Cycle Engine (VCE) parameters plays a key role in achieving low specific fuel consumption (SFC) of fighters. However, the selection of VCE parameters is a continuous multimodal issue involving substantial local optima, so that most swarm intelligence (SI) algorithms are easily trapped into local optimal solutions, and cannot obtain satisfactory performance. To address this problem, an improved moth flame optimization algorithm with adaptive Levy-Flight perturbations (ALFMFO) is proposed. In ALFMFO, the current population aggregation status can be accurately judged based on the difference in fitness variance between two successive moth generations. According to the population aggregation status, the Levy-Flight disturbance strategy can adaptively adjust the perturbation probability to enhance the ability of ALFMFO to escape from local optimal solutions and realize the minimum SFC optimization of VCE. Experimental results suggest that ALFMFO is effective and superior to other compared SI algorithms in terms of accuracy and robustness.
Highlights
Thermodynamic cycle characteristics of conventional aircraft turbine engines and turbofan engines are changeless, so that one of aircraft turbine engines and turbofan engines can work in only one mode
In order to verify the competitive performance of ALFMFO, it is compared with the other six prominent algorithms
It is worth mentioning that the Lévy-Flight Moth-Flame Optimization algorithm (LMFO) is set to verify the effectiveness of the adaptively Lévy-Flight disturbance strategy proposed in this paper, and LMFO is a control group which perturbs all moth individuals at each iteration, but the proposed ALFMFO algorithm adaptively perturbs the moths according to the moth gathering situation
Summary
Thermodynamic cycle characteristics of conventional aircraft turbine engines and turbofan engines are changeless, so that one of aircraft turbine engines and turbofan engines can work in only one mode. A lot of research work has been done on SI optimization algorithms, to the best of our knowledge, the MFO and its modified algorithms still encounter numerous challenges in the minimum SFC optimization of VCE, and these algorithms generally suffer from poor robustness, low accuracy, and premature convergence into local optimal when solving this problem. To escape from the local optimal and improve the global search capability, the Lévy-Flight disturbance strategy [38] is added to the algorithm, and the number of disturbed moths is adjusted dynamically according to the difference in fitness variance between two successive generations. A novel adaptive disturbance mechanism is proposed to balance the tendency between exploitation and exploration In this way, some moths are perturbed by the Lévy-Flight strategy according to the current population state.
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