  This paper proposes an optimization algorithm based on how human fight and
learn from each duelist. Since this algorithm is based on population, the
proposed algorithm starts with an initial set of duelists. The duel is to
determine the winner and loser. The loser learns from the winner, while the
winner try their new skill or technique that may improve their fighting
capabilities. A few duelists with highest fighting capabilities are called as
champion. The champion train a new duelists such as their capabilities. The new
duelist will join the tournament as a representative of each champion. All
duelist are re-evaluated, and the duelists with worst fighting capabilities is
eliminated to maintain the amount of duelists. Two optimization problem is
applied for the proposed algorithm, together with genetic algorithm, particle
swarm optimization and imperialist competitive algorithm. The results show that
the proposed algorithm is able to find the better global optimum and faster
iteration.

Neural and Evolutionary Computing