In our previous work, Fitness Predator Optimizer (FPO) is proposed to avoid premature
convergence for multimodal problems. In FPO, all of the particles are seen as predators.
Only the competitive, powerful predator that are selected as an elite could achieve the
limited opportunity to update. The elite generation with roulette wheel selection could
increase individual independence and reduce rapid social collaboration. Experimental results
show that FPO is able to provide excellent performance of global exploration and
local minima avoidance simultaneously. However, to the higher dimensionality of multimodal
problem, the slow convergence speed becomes the bottleneck of FPO. A dynamic
team model is utilized in FPO, named DFPO to accelerate the early convergence rate. In
this paper, DFPO is more precisely described and its variant, DFPO-r is proposed to improve
the performance of DFPO. A method of team size selection is proposed in DFPO-r
to increase population diversity. The population diversity is one of the most important
factors that determines the performance of the optimization algorithm. A higher degree
of population diversity is able to help DFPO-r alleviate a premature convergence. The
strategy of selection is to choose team size according to the higher degree of population
diversity. Ten well-known multimodal benchmark functions are used to evaluate the solution
capability of DFPO and DFPO-r. Six benchmark functions are extensively set to 100
dimensions to investigate the performance of DFPO and DFPO-r compared with LBest
PSO, Dolphin Partner Optimization and FPO. Experimental results show that both DFPO
and DFPO-r could demonstrate the desirable performance. Furthermore, DFPO-r shows
better robustness performance compared with DFPO in experimental study.
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