**arXiv ID:** 1906.02010 **Authors:** Sujit Pramod Khanna, Alexander Ororbia **Published:** 2019-05-26T10:45:58Z **Abstract:** We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess. The information produced by each individual agent can be combinedin variou...
Scanned 9/11/2026
Install to Claude Code
Skills Directorynpx -y skills add hiyenwong/ai_collection --skill a-hybrid-algorithm-for-metaheuristic-optimization --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of A Hybrid Algorithm For Metaheuristic Optimization?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-a-hybrid-algorithm-for-metaheuristic-optimization)More formats (shields.io, HTML) on the badges page.
# A Hybrid Algorithm for Metaheuristic Optimization
**arXiv ID:** 1906.02010
**Authors:** Sujit Pramod Khanna, Alexander Ororbia
**Published:** 2019-05-26T10:45:58Z
**Abstract:**
We propose a novel, flexible algorithm for combining together metaheuristicoptimizers for non-convex optimization problems. Our approach treatsthe constituent optimizers as a team of complex agents that communicateinformation amongst each other at various intervals during the simulationprocess. The information produced by each individual agent can be combinedin various ways via higher-level operators. In our experiments on keybenchmark functions, we investigate how the performance of our algorithmvaries with respect to several of its key modifiable properties. Finally,we apply our proposed algorithm to classification problems involving theoptimization of support-vector machine classifiers.
## Skill Description
This skill is generated from the arXiv paper: A Hybrid Algorithm for Metaheuristic Optimization (1906.02010).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:1906.02010](http://arxiv.org/abs/1906.02010v1)
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
This skill provides comprehensive analysis of competitor SEO and GEO strategies, revealing what's working in your market and identifying opportunities to outperform the competition.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review...
Use when an operation issue is a Paperclip cursor-window, distill, or backfill — `operationType: "distill"` or `"backfill"` and the body references a Paperclip source bundle for a project or root issue. Turn raw Paperclip activity into a wiki-insightful project page, decisions log, and history note. This skill exists specifically to replace the stiff, datestamp-heavy templated output that the deterministic distiller produces.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publi...
Semantic search, similar content discovery, and structured research using Exa API
