Cyclic instability is a problem that, despite being shown to affect intelligent environments and in general any rule-based system, the strategies available to prevent it are still limited and mostly focused on centralized approaches. These approaches are based on topological properties of the Interaction Network (IN) associated, and locking a set of agents. In this paper we present a comparative study of the performance of different optimization techniques when solving the problem of cyclic instability in synthetic scenarios. Instead of using the Interaction Network of the System (which can be computationally expensive, specially in very dense systems). We introduced the concept of Average Change of the System (ACS) in order to measure the oscillatory behavior of the system and an optimization strategy. In particular Particle Swarm Optimization (PSO), Micro-Particle Swarm Optimization (μ-PSO), Bee Swarm Optimization (BSO), Artificial Immune Systems (AIS) and Genetic Algorithms (GA) were considered. The results found are very promising, as they can successfully prevent unwanted oscillations. Additionally, some of these strategies could be implemented using parallel and distributed processing, in order to be used in real-time scenarios.