docs: Zensical documentation site with scientific visualization suite

[Copilot]

Creates a modern documentation site using Zensical (next-gen SSG from squidfunk team) with Material Design 3 theming and mkdocstrings integration for auto-generated API docs.

Configuration

• docs/zensical.yml with deep purple + teal palette, dark/light toggle
• MathJax for LaTeX equations, Mermaid for diagrams
• mkdocstrings with Google-style docstring parsing

Documentation Structure

• Guide: Installation, quickstart, advanced usage
• Algorithms: All 10 categories (swarm intelligence, evolutionary, gradient-based, classical, metaheuristic, physics-inspired, social-inspired, probabilistic, constrained, multi-objective)
• API Reference: Auto-generated from docstrings for 100+ optimizer classes
• Benchmarks: Methodology (COCO/IOHprofiler standards), functions catalog, results dashboard placeholder

Dependencies

[project.optional-dependencies]
docs = [
    "zensical>=0.0.11",
    "mkdocstrings[python]>=0.27",
    "mkdocs-material>=9.6",
]

Build

uv sync --extra docs
uv run zensical build   # or: uv run mkdocs build
uv run mkdocs serve     # local preview

Original prompt

---

This section details on the original issue you should resolve

<issue_title>docs: Zensical Documentation Site with Scientific Visualization Suite</issue_title>
<issue_description># Zensical Documentation Site with Scientific Visualization Suite

Summary

Create a modern documentation site using Zensical (the next-gen SSG from the Material for MkDocs team), leveraging its modern design system with Material Design 3 color palette compatibility, featuring state-of-the-art scientific visualization for optimizer benchmark results inspired by COCO/IOHprofiler standards.

Why Zensical? The squidfunk team (creators of Material for MkDocs) launched Zensical in November 2025 as their next-generation static site generator. It addresses MkDocs' architectural limitations and supply chain risks (unmaintained since Aug 2024) while providing 5x faster rebuilds, blazing-fast Disco search, and full mkdocs.yml compatibility.

---

Technology Stack

Core Framework

Component	Technology	Rationale
SSG	Zensical	Next-gen from squidfunk team, Rust+Python, 5x faster rebuilds
Build Engine	ZRX	Differential build engine - backbone of Zensical (Open Source)
Theme	Modern Design	Built-in brandable design, breaks from Material Design aesthetic
Colors	Material Design 3	Built-in deep purple + teal palette with dark/light toggle
Search	Disco	Blazing-fast client-side search with filtering & aggregation
Math	MathJax/KaTeX	Full LaTeX support for optimization equations
Diagrams	Mermaid	Native support inherited from Material for MkDocs
API Docs	mkdocstrings	Tim (@pawamoy) joining Zensical team for API reference

Built-in Color Palette (Zero Custom CSS)

Zensical inherits Material for MkDocs' battle-tested color system:

# zensical.yml (compatible with mkdocs.yml)
theme:
  palette:
    # Light mode
    - media: "(prefers-color-scheme: light)"
      scheme: default
      primary: deep purple
      accent: teal
      toggle:
        icon: material/brightness-7
        name: Switch to dark mode
    # Dark mode  
    - media: "(prefers-color-scheme: dark)"
      scheme: slate
      primary: deep purple
      accent: teal
      toggle:
        icon: material/brightness-4
        name: Switch to light mode

Deep Purple + Teal Palette:

• deep purple (#673AB7) - Primary: headers, links, navigation accents
• teal (#009688) - Accent: buttons, highlights, interactive elements
• slate - Dark mode background scheme
• Automatic WCAG AA contrast compliance

Zensical-Specific Advantages

Feature	Benefit for useful-optimizer
ZRX Differential Builds	Only rebuild changed pages - critical for 100+ algorithm docs
Disco Search	Filtering by category, aggregation, fuzzy matching for 100+ algorithms
mkdocs.yml Compatibility	Minimal migration effort from existing MkDocs projects
Module System (Early 2026)	Replace plugins with deterministic, parallelizable modules
Component System (2026)	Custom Vue-like components for scientific visualizations
CommonMark Rust Parser	Significant Markdown performance improvements
mkdocstrings Integration	Native Python docstring extraction (Google-style)

---

Scientific Visualization Architecture

Overview

Three-tier visualization system matching academic benchmarking standards (COCO, IOHprofiler, Nevergrad):

┌─────────────────────────────────────────────────────────────────┐
│                    TIER 1: Scientific Standard                   │
│  ┌─────────┐ ┌──────────────┐ ┌────────┐ ┌──────────┐ ┌───────┐ │
│  │  ECDF   │ │ Convergence  │ │ Violin │ │ Friedman │ │Wilcox │ │
│  │  Curve  │ │ w/Confidence │ │  Plot  │ │ Heatmap  │ │Matrix │ │
│  └─────────┘ └──────────────┘ └────────┘ └──────────┘ └───────┘ │
├─────────────────────────────────────────────────────────────────┤
│                    TIER 2: Interactive Exploration               │
│  ┌─────────┐ ┌──────────────┐ ┌──────────┐ ┌─────────┐ ┌──────┐ │
│  │   3D    │ │   Search     │ │ Parallel │ │ Perf.   │ │Radar │ │
│  │Landscape│ │  Trajectory  │ │  Coords  │ │ Profile │ │Chart │ │
│  └─────────┘ └──────────────┘ └──────────┘ └─────────┘ └──────┘ │
├─────────────────────────────────────────────────────────────────┤
│                    TIER 3: Dashboard Integration                 │
│  ┌─────────────────────────────────────────────────────────────┐ │
│  │  Unified Dashboard: Algorithm Selection → Multi-View Panel  │ │
│  │  Statistical Summary: Avg | Std | Best | Worst | Time | FE  │ │
│  └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘

---

Tier 1: Scientific Standard Visualizations

1.1 Empirical Cumulative Distribution Function (ECDF)

The ...

• Fixes docs: Zensical Documentation Site with Scientific Visualization Suite #55

---

💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.

[pawamoy]

@Anselmoo Please instruct your AI model to avoid GitHub mentions! Or at least remove them manually. Thanks.