A cultural evolution with a modified selection function and adaptive α-cognition procedure for numerical optimization

In recent years, several population-based evolutionary and swarm algorithms have been developed and used in the literature. This work introduces an improved Cultural Algorithm with a modified selection function and a dynamic α-cognition procedure to handle a variety of challenging numerical optimization problems. The modified selection function is used to support a balanced evolutionary search. A process that starts with a clearer exploration early in the search process and gradually begins to focus on exploitation towards the end of the search process. This work uses the elites of each knowledge source that are at a certain distance from each other. The dynamic α-cognition procedure assists in providing effective learning of individuals through preserving the diversity of the population during the evolution process. In this procedure, each individual is able to learn from the top α% individuals controlled by its knowledge source in the belief space, where the proportion of the affecting subpopulation (α) is adaptively modified during the evolution. The performance of the proposed work has been evaluated on the CEC’2010 and CEC’2013 benchmark suites developed for the special sessions on large-scale global optimization problems. An appropriate comparative study with the best results in the literature is presented. The results confirm how the merits of the improved Cultural Algorithm can achieve superior performance over other cutting-edge algorithms for these data sets.