NichingEDA: Utilizing the diversity inside a population of EDAs for continuous optimization

Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian models as an example, EDAs based on single Gaussian distribution have good performance on solving simple unimodal functions and multimodal functions whose landscape has an obvious trend towards the global optimum. But they have difficulties in solving multimodal functions with irregular landscapes, such as wide basins, flat plateaus and deep valleys. Gaussian mixture model based EDAs have been developed to remedy this disadvantage of single Gaussian based EDAs. A general framework NichingEDA is presented in this paper from a new perspective to boost single model based EDAs’ performance. Through adopting a niching method and recombination operators in a population of EDAs, NichingEDA significantly boosts the traditional single model based EDAs’ performance by making use of the diversity inside the EDA population on hard problems without estimating a precise distribution. Our experimental studies have shown that NichingEDA is very effective for some hard global optimization problems, although its scalability to high dimensional functions needs improving. Analyses and discussions are presented to explain why NichingEDA performed well/poorly on certain benchmark functions.