An algorithm comparison for dynamic optimization problems

This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based cooperative strategies. The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions. For each problem, a wide variety of configuration variations have been used, emphasizing the influence of dynamism, and using a full-factorial experimental design. The results give an interesting overview of the properties of the algorithms and their applicability, and provide useful hints to face new problems of this type with the best algorithmic approach. Additionally, a recently introduced methodology for comparing a high number of experimental results in a graphical way is used.