2015/12/02 by Totok Ruki Biyanto, Totok R. Biyanto, Henokh Yernias Fibrianto +10
Computer Science · Engineering · #Artificial Intelligence in Games #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Human Motion and Animation #Neural and Evolutionary Computing (cs.NE) #cs.NE
paper · pdf · doi:10.48550/arxiv.1512.00708
This paper under submission to the Journal of Swarm and Evolutionary Computation, consist of 7 pages and 11 figures
arxiv created 2015/12/02 · openalex publication_date 2015/12/02 · arxiv updated 2015/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.