Multi-objective firefly algorithm with multi-strategy integration

In the optimization process of multi-objective firefly algorithm, population is easy to fall into local optimum, which leads to poor population distribution and convergence. In order to solve this problem, this article proposes a multi-objective firefly algorithm with multi-strategy integration (MOFA-MSI). First, in order to improve the distribution of population, MOFA-MSI proposes a cloning strategy, which calculates the distribution degree of individuals in population, clones them according to their distribution degree, and local mutation in the cloned individual produce a new population with good distribution. Then, in order to maintain the convergence of population, a position updating strategy based on non-dominated sorting is proposed. The new population after local mutation are performed by non-dominated sorting, and the fireflies with higher rank guide the fireflies with lower rank to fly, and then new population are generated by global mutation after position updated. Finally, the greedy strategy is adopted to select solutions with better distribution and convergence and store them in external files. In the experimental part, different types of test problems are used to test the performance of each algorithm, and MOFA-MSI is compared with three classical and four new multi-objective evolutionary algorithms. The results show that MOFA-MSI is superior to other seven algorithms in terms of the distribution and convergence of population.