Diversity Enhanced Optimization Based on Communication Strategy Particles and Pollens

Easy convergence to a local optimum, rather than global optimum, could unexpectedly happen in practical multimodal optimization problems. The enhanced diversity agent in optimal algorithms is one of the solutions to this issue. This paper proposes a novel optimization algorithm, namely DPP, based on the communication of the particles in Particle swarm optimization (PSO), with the pollen in Flower pollination algorithm (FPA) to solve the multimodal optimization problems. A new communication strategy for Particles and Pollens is to take advantages of the strength points of each side type of algorithms to explore and exploit the algorithm diversity. Six multimodal benchmark functions are used to verify the convergence behavior, the accuracy, and the speed of the proposed algorithm. Experimental results show that the proposal increases the accuracy more than the existing algorithms.