Multi-swarm cooperative multi-objective bacterial foraging optimisation
by Ben Niu; Jing Liu; Lijing Tan
International Journal of Bio-Inspired Computation (IJBIC), Vol. 13, No. 1, 2019

Abstract: This paper proposes a novel multi-objective algorithm which is based on the concept of master-slave swarm, namely multi-swarm cooperative multi-objective bacterial foraging optimisation (MCMBFO). In MCMBFO, the multi-swarm cooperative operation which involves several slave-swarms and a master-swarm is developed to accelerate the bacteria to come closer to the true Pareto front. With regard to slave-swarms, each of them evolves collaboratively with others during the steps of chemotaxis and reproduction, using information communication mechanism and cross-reproducing approach respectively to enhance the convergence rate. At the same time, bacteria in the master-swarm are all non-dominated individuals selected from slave-swarms. They evolve based on non-dominated sorting approach and crowding distance operation, aiming to improve the accuracy and diversity of solutions. The superiority of MCMBFO is confirmed by simulation experiments using several test problems and performance metrics chosen from prior representative studies. Simulation results illustrate that MCMBFO is considerably competitive for most of the cases, especially in terms of converging to the true Pareto front.

Online publication date: Wed, 06-Feb-2019

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