Paralleled fitness predator optimizer with modified dynamic virtual team for multimodal problems

To avoid premature convergence by maintaining diversity in the population, a new algorithm, Fitness Predator Optimizer (FPO), is proposed based on the concepts of predators. However, the slow convergence speed in the early iteration becomes the bottleneck to restrict the improvement of the FPO. In this paper, a modified dynamic virtual team for Fitness Predator Optimizer is proposed with the aim of accelerating the early convergence rate and improving the global searching capability for multimodal problems. The virtual dynamic team is a self-organization team that according to the nearest neighbour clustering. Each individual automatically is denoted as the team leader. The main function of the team leader is to obtain the neighbour team's best experience. It is worth noting that one of a team members may belong to another group concurrently. The expansion of individual best experience and the team best experience is carried out by this kind of multiple memberships. Consequently a paralleled exchanging information system is built on the modified dynamic virtual team to strengthen on the global searching capability during the early convergence process. Then an improved Dynamic Fitness Predator Optimizer (DFPO) with the modified virtual team is proposed in this paper. We extensively evaluated the DFPO and compared it with FPO, Dolphin Partner Optimization (DPO) and Standard Particle Swarm Optimization (SPSO), with respect to both running time and global solution capability. Experimental results show that the proposed DFPO could demonstrate the desirable performance for multimodal problems.