A novel hybrid algorithm based on PSO and FOA for target searching in unknown environments

In unknown environments, multiple-robot cooperation for target searching is a hot and difficult issue. Swarm intelligence algorithms, such as Particle Swarm Optimization (PSO) and Fruit Fly Optimization Algorithm (FOA), are widely used. To overcome local optima and enhance swarm diversity, this paper presents a novel multi-swarm hybrid FOA-PSO (MFPSO) algorithm for robot target searching. The main contributions of the proposed method are as follows. (1) The improved FOA (IFOA) provides a better value for the improved PSO (IPSO) to find the next optimal robot position value. (2) Multi-swarm strategy is introduced to enhance the diversity and achieve an effective exploration to avoid premature convergence and falling into local optima. (3) An escape mechanism named MSCM (Multi-Scale Cooperative Mutation) is used to address the limitation of local optima and enhance the escape ability for obstacle avoidance. All of the aspects mentioned above lead robots to the target without falling into local optima and allow the search mission to be performed more quickly. Several experiments in four parts are performed to verify the better performance of MFPSO. The experimental results show that the performance of MFPSO is much more significant than that of other current approaches.