Application of the Brain Drain Optimization Algorithm to the N-Queens Problem
This is an incremental application of a known metaheuristic to a classic benchmark problem, potentially useful for researchers in combinatorial optimization.
The paper applied the Brain Drain Optimization algorithm to solve the N-Queens combinatorial optimization problem, achieving fewer threats and better objective function values compared to established metaheuristics like PSO, GA, and ICA.
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.