Chimpanzee leader election optimization

Abstract

Solving the convergence problem in the world of computing is needed to find an optimization solution. Many researchers propose a meta-heuristic approach in solving problems quickly using the results of the approach. This method is used because of its flexibility and fast search rate. Each meta-heuristic method has its own characteristics and is not necessarily able to solve the problem for all cases. This paper proposes a meta-heuristic method that is inspired by the way in which leaders are chosen in the chimpanzee community. The leader in the chimpanzee community is always the alpha male chimpanzee and has relationships with all chimps, both male and female. The principle of Chimpanzee Leader Election Optimization (CLEO) is the phase of improving performance and social relations with community members. The chimp performance improvement phase is based on the age aspect, which affects the memorize’s ability of the chimps, which are inverted U-shaped. Meanwhile, the social relations/mating phase has been analogized in this CLEO algorithm. This paper has also evaluated the performance of the proposed algorithm, both to find the optimal value in the minimum and maximum search. This algorithm has also been compared with various other meta-heuristic algorithms. The results of the CLEO algorithm are quite competitive with other meta-heuristic algorithms in multi-high dimensional.