Application of the Brain Drain Optimization Algorithm to the N-Queens Problem

TL;DR

The paper tackles the $N$-Queens problem using a swarm-based optimization approach named BRADO. It integrates a problem-specific cost function with a TOPSIS-based multicriteria decision making process to tune BRADO and compare it against PSO, GA, ICA, ILS, and LS. BRADO consistently outperforms these baselines in solution quality and robustness (fewer threats and better objective values) across problem sizes from 8 to 1000 queens, with parameter tuning guided by TOPSIS. The results support BRADO as a general-purpose solver for combinatorial optimization and suggest broader applicability to AI domains.

Abstract

This paper introduces the application of the Brain Drain Optimization algorithm -- a swarm-based metaheuristic inspired by the emigration of intellectual elites -- to the N-Queens problem. The N-Queens problem, a classic combinatorial optimization problem, serves as a challenge for applying the BRADO. A designed cost function guides the search, and the configurations are tuned using a TOPSIS-based multicriteria decision making process. BRADO consistently outperforms alternatives in terms of solution quality, achieving fewer threats and better objective function values. To assess BRADO's efficacy, it is benchmarked against several established metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), Iterated Local Search (ILS), and basic Local Search (LS). The study highlights BRADO's potential as a general-purpose solver for combinatorial problems, opening pathways for future applications in other domains of artificial intelligence.