A Double Evolutionary Learning Moth-Flame Optimization for Real-Parameter Global Optimization Problems

Abstract

The original moth-flame optimization (MFO) algorithm neither generates high-performance flames nor utilizes the flames to offer enough effective search guidance for moths in solution spaces, causing the degeneration of the global search capability and convergence speed in confronting complicated problems. To overwhelm those imperfections, this paper proposes a double-evolutionary learning MFO algorithm (DELMFO), where two different evolutionary learning strategies, namely, the differential evolution flame generation (DEFG) and dynamic flame guidance (DFG) strategy, are presented to generate high-performance flames and dynamically guide the search of moths, respectively. By constructing the cascading collaboration between DEFG and DFG, the DELMFO offers a positive feedback channel that makes the personal best historical solutions (PBHSs), flames, and moths promote each other. This improves the global search capability and accelerates convergence speed. The DELMFO is compared with six MFO algorithms and nine popular stochastic optimization algorithms on the CEC2013 test suite. Furthermore, the DELMFO also is further compared with 10 stochastic optimization algorithms on the CEC2017 test suite. Experimental results show that the DELMFO obtains the competitive performance on the global search capability, convergence speed, and scalability among all the algorithms.