Cloud estimation of distribution algorithm with quasi-oppositional learning and preference order ranking for multi-objective optimisation

Abstract

Cloud estimation of distribution algorithm is a cloud model-inspired optimisation algorithm. In this paper, by incorporating quasi-oppositional learning and using preference order ranking into the algorithm, it is extended for solving multi-objective optimisation problems. In order to achieve a better approximation of the current candidate solution, quasi-oppositional learning is used for population initialisation and new individual generation. Three digital characteristics from the current population are first estimated by backward cloud generator. Afterwards, forward cloud generator is used to generate current offspring population according to three digital characteristics. The population with the current population and current offspring population is sorted based on preference order, and the best individuals are selected to form the next population. The proposed algorithm is tested to compare with some other algorithms using a set of benchmark functions. The experimental results show that the algorithm is effective on the benchmark functions.