Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel

TL;DR

The paper introduces the Duelist Algorithm, a population-based optimization method inspired by human duelist training, demonstrating improved efficiency and effectiveness in finding global optima compared to existing algorithms.

Contribution

It presents a novel optimization algorithm inspired by duelist training, integrating champion-led learning and tournament elimination, with competitive results against established methods.

Findings

Achieves better global optima

Converges faster than comparison algorithms

Effective on benchmark optimization problems

Abstract

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.