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

• URL: http://arxiv.org/abs/2504.18953v1
• Date: Sat, 26 Apr 2025 15:32:17 GMT
• Title: Application of the Brain Drain Optimization Algorithm to the N-Queens Problem
• Authors: Sahar Ramezani Jolfaei, Sepehr Khodadadi Hossein Abadi,
• Abstract summary: 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.<n>The N-Queens problem, a classic optimization problem, serves as a challenge for applying the BRADO.<n> BRADO consistently outperforms alternatives in terms of solution quality, achieving fewer threats and better objective function values.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Noise to the Rescue: Escaping Local Minima in Neurosymbolic Local Search [50.24983453990065]
  We show that applying BP to Godel logic, which represents conjunction and disjunction as min and max, is equivalent to a local search algorithm for SAT solving.<n>We propose the Godel Trick, which adds noise to the model's logits to escape local optima.
  arXiv  Detail & Related papers  (2025-03-03T18:42:13Z)
• A Random-Key Optimizer for Combinatorial Optimization [0.0]
  The Random-Key hubs (RKO) is a versatile and efficient local search method tailored for optimization problems. Using the random-key concept, RKO encodes solutions as vectors of random keys that are subsequently decoded into feasible solutions via problem-specific decoders. The RKO framework is able to combine a plethora of classic metaheuristics, each capable of operating independently or in parallel, with solution sharing facilitated through an elite solution pool.
  arXiv  Detail & Related papers  (2024-11-06T22:23:29Z)
• Quality-Diversity Algorithms Can Provably Be Helpful for Optimization [24.694984679399315]
  Quality-Diversity (QD) algorithms aim to find a set of high-performing, yet diverse solutions. This paper tries to shed some light on the optimization ability of QD algorithms via rigorous running time analysis.
  arXiv  Detail & Related papers  (2024-01-19T07:40:24Z)
• SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving [64.38649623473626]
  Large Language Models (LLMs) have driven substantial progress in artificial intelligence. We propose a novel framework called textbfSEquential subtextbfGoal textbfOptimization (SEGO) to enhance LLMs' ability to solve mathematical problems.
  arXiv  Detail & Related papers  (2023-10-19T17:56:40Z)
• High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems [3.6406488220483326]
  A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation. We propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. We show that our proposed method is superior in terms of speed and the attained optimal solutions.
  arXiv  Detail & Related papers  (2022-12-03T09:22:54Z)
• Socio-cognitive Optimization of Time-delay Control Problems using Evolutionary Metaheuristics [89.24951036534168]
  Metaheuristics are universal optimization algorithms which should be used for solving difficult problems, unsolvable by classic approaches. In this paper we aim at constructing novel socio-cognitive metaheuristic based on castes, and apply several versions of this algorithm to optimization of time-delay system model.
  arXiv  Detail & Related papers  (2022-10-23T22:21:10Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set [48.57120672468062]
  This paper introduces an efficient algorithm for the Maximum Independent Set (MIS) problem, incorporating two innovative techniques.<n>The proposed algorithm outperforms state-of-the-art algorithms in terms of solution quality, computational efficiency, and stability.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• Simulation-guided Beam Search for Neural Combinatorial Optimization [13.072343634530883]
  We propose simulation-guided beam search (SGBS) for neural optimization problems. We hybridize SGBS with efficient active search (EAS), where SGBS enhances the quality of solutions backpropagated in EAS. We evaluate our methods on well-known CO benchmarks and show that SGBS significantly improves the quality of the solutions found under reasonable assumptions.
  arXiv  Detail & Related papers  (2022-07-13T13:34:35Z)
• Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
  We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
  arXiv  Detail & Related papers  (2020-01-29T09:40:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.