QMD is an open‑source, MIT‑licensed local search engine written in TypeScript that combines BM25, vector embeddings via a GGUF model, and an LLM reranker, allowing natural‑language queries over thousands of markdown files without network calls, and can be installed with just three commands.
This article presents a bi‑objective inventory allocation model for guaranteed‑delivery advertising that simultaneously maximizes impressions for new orders and balances supply distribution, and introduces a fast alternating local‑search algorithm (BOLS) that outperforms popular multi‑objective evolutionary algorithms and the commercial solver Gurobi in extensive experiments.
The paper introduces LS‑IMP, a two‑stage local‑search algorithm with four novel operators that efficiently solves guaranteed‑delivery advertising inventory allocation under non‑convex multilinear media‑preference constraints, consistently outperforming commercial solvers and heuristics in solution quality and speed on real‑world datasets.
This article explains the Butterfly Optimization Algorithm (BOA), covering its biological inspiration, core concepts such as scent-based global and local search, key parameters, iteration process, and provides a complete Python implementation with example usage and visualisation of convergence.
