Self-adaptive differential evolution based on PSO learning strategy

Differential evolution (DE) is an effective and efficient optimization algorithm that has been successfully applied to many problems. However, the DE performance significantly depends on the elaborate settings of its parameters. Designers of DE usually spend great efforts to find proper parameter settings because good parameter values usually vary with different problems. In order to enhance the efficiency and robustness of DE, this paper proposes a novel DE algorithm, PLADE, which uses the learning mechanism in particle swarm optimization (PSO), termed as PSO-Learning (PL) strategy, to adaptively control the DE parameters. PLADE encodes the DE parameters into each individual and evolve the parameters during the evolutionary process. The individuals that achieve good fitness and survive in the evolution imply good parameter settings, the poor individuals use the PL strategy to let their parameters learn from the parameters in the good individuals. With such a PL based parameter self-adaptation strategy, PLADE can evolve the parameters to better values and can adapt the parameters to match the requirements of different evolutionary states and different optimization problems. PLADE is tested by six benchmark functions with unimodal and multimodal characteristics. Experimental results show that PLADE not only outperforms conventional DE with fixed parameter settings, in terms of solution quality, convergence speed, and algorithm reliability, but also is better than or at least comparable to some other state-of-the-art adaptive DE variants.