A New Multiagent Algorithm for Dynamic Continuous Optimization

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this article, a new multiagent algorithm is proposed to solve dynamic problems. This algorithm is based on multiple trajectory searches and saving the optima found to use them when a change is detected in the environment. The proposed algorithm is analyzed using the Moving Peaks Benchmark, and its performances are compared to competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of the proposed algorithm, even in multimodal environments.