Abstract
A dandelion algorithm (DA) is a recently-proposed intelligent optimization algorithm and shows an excellent performance in solving function optimization problems. However, like other intelligent algorithms, it converges slowly and falls into local optima easily. To overcome these two flaws, a dandelion algorithm with probability-based mutation (DAPM) is proposed in this paper. In DAPM, both Gaussian and Levy mutations can be used interchangeably according to a given probability model. In this paper, three probability models are discussed, namely linear, binomial, and exponential models. The experiments show that DAPM achieves better overall performance on standard test functions than DA.
Highlights
DANDELION ALGORITHM we introduce dandelion algorithm (DA)
dandelion algorithm with probability-based mutation (DAPM) BASED ON EXPONENTIAL MODEL E can be computed by the following exponential model: E = e−Tc2/Tm2ax where the value of E changes exponentially. We call this kind of DAPM as an exponential model, denoted as DAPME
DAPML, DAPMB and DAPME are better than Artificial Bee Colony (ABC), SPSO2011 and CMA-ES
Summary
Computational intelligence plays a powerful role in information processing, decision making and knowledge management. The collective intelligent behavior of insects or animal groups in nature such as birds, ants, fish, and bees has attracted the attention of researchers. Entomologists have studied it to simulate biota, and engineers have used the related models as a framework to solve some real-world complex problems Through those complex individual interactions without supervision, group intelligence can be obtained. DA establishes a mathematical model by simulating the behavior of dandelion sowing, and uses a parallel search method by introducing random factors and selection strategies. It is capable of solving complex problems. This paper proposes the dandelion algorithm with probability-based mutation (DAPM) by adding the Gaussian mutation to DA since Gaussian mutation can ensure a strong local ability of exploitation.
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