Editorial

Swarm intelligence is a discipline that deals with the natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization.Lately, swarm intelligence algorithms are becoming increasingly important in the face of the complexity of today's demanding applications.This issue aims to present a collection of recent advances in the swarm intelligence algorithms.On the basis of a peer-review process, six papers are accepted to be included in the thematic issue, covering various algorithms including particle swarm optimization, monarch butterfly optimization, moth search algorithm, evolutionary computation, and cuckoo search.Surrogate assisted meta-heuristic algorithms have received increasing attention over the past years, due to the fact that numerous real-world optimization problems are computationally expensive.However, most existing surrogate assisted meta-heuristic algorithms are designed for small or medium scale problems.In the first paper of this issue, Chaoli Sun et al. propose a fitness approximation assisted competitive swarm optimizer for the optimization of large scale expensive problems.Different from regular surrogate assisted evolutionary algorithms that use a computational model for approximating the fitness, this paper uses estimates of fitness based on the positional relationship between the individuals in the competitive swarm optimizer.Empirical study on seven widely used benchmark problems with 100 and 500 decision variables show that the proposed fitness approximation assisted competitive swarm optimizer can achieve competitive performance on a limited computational budget.