Many real-world optimization problems are dynamic (time dependent) and require an algorithm that is able to track continuously a changing optimum over time. In this paper, we propose a new algorithm for dynamic continuous optimization. The proposed algorithm is based on several coordinated local searches and on the archiving of the optima found by these local searches. This archive is used when the environment changes. The performance of the algorithm is analyzed on the Moving Peaks Benchmark and the Generalized Dynamic Benchmark Generator. Then, a comparison of its performance to the performance of competing dynamic optimization algorithms available in the literature is done. The obtained results show the efficiency of the proposed algorithm.

The proposed multiple local search algorithm effectively tracks changing optimums in dynamic real-world optimization problems, outperforming competing algorithms on Moving Peaks and Generalized Dynamic Benchmark Generator benchmarks.

A multiple local search algorithm for continuous dynamic optimization

Key Takeaway: The proposed multiple local search algorithm effectively tracks changing optimums in dynamic real-world optimization problems, outperforming competing algorithms on Moving Peaks and Generalized Dynamic Benchmark Generator benchmarks.