A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems

TLDR

Evolutionary algorithms, particle swarm optimization, and differential evolution have been extended over decades, with DE recently demonstrating superior performance in real‑world applications. The study evaluates the general applicability of DE, PSO, and EAs as numerical optimization techniques. The comparison uses a suite of 34 widely used benchmark problems. DE generally outperforms PSO and EAs, though on two noisy functions both DE and PSO were outperformed by the EA.

Abstract

Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.