Metaheuristic & AI Search Benchmark (CS-AI-N3)
From-Scratch Comparison of 8 Optimization Algorithms on Continuous & Discrete Landscapes
A from-scratch NumPy implementation and empirical benchmark of 8 search and metaheuristic algorithms (PSO, ABC, Firefly, Cuckoo Search, GA, Hill Climbing, ACO, SA) evaluated across 10D/30D Rastrigin and 20/50-item 0/1 Knapsack problems.
The Technical Bottleneck
Swarm intelligence and evolutionary algorithms exhibit markedly different exploration-exploitation trade-offs across continuous multimodal functions versus discrete combinatorial optimization.
Why Existing Baselines Fail
Implementing eight classic and modern metaheuristics from scratch without black-box optimization libraries enables controlled comparison of convergence speed, variance across seeds, and dimensionality scaling.
Core Formulation & Insight
Implemented Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm, Cuckoo Search, Genetic Algorithm (GA), Hill Climbing, Ant Colony Optimization (ACO), and Simulated Annealing (SA) in pure Python/NumPy, running 10 independent trials (population 50, 50 iterations) per benchmark.
Architectural Design
Unified benchmark runner and visualization suite for 8 continuous and discrete optimization algorithms.
1. Eight From-Scratch Algorithm Implementations
PSO, ABC, Firefly, Cuckoo Search, GA, Hill Climbing, ACO, and Simulated Annealing in NumPy.
2. Continuous & Discrete Benchmark Landscapes
Evaluates multimodal Rastrigin (10D & 30D) and combinatorial 0/1 Knapsack (20 & 50 items).
3. Multi-Run Convergence & Robustness Analytics
Aggregates 10-run mean/std trajectories, boxplots, computational complexity, and trade-off plots.
Technical Stack & Systems Engineering
Implementation Highlights
- →Zero external heuristic libraries—all 8 algorithms and visualization pipelines written from mathematical definitions.
Datasets & Protocol
Datasets & Corpora
- Rastrigin Function (10D, 30D) & 0/1 Knapsack Instances (20, 50 items)
Evaluation Protocol
10 independent runs per algorithm with population size 50 and 50 iterations.
Tracked Evaluation Metrics
Benchmarked 8 metaheuristic algorithms across continuous and discrete optimization regimes with full convergence and robustness diagnostics.
PSO, ABC, FA, CS, GA, HC, ACO, SA
Rastrigin (10D/30D) & 0/1 Knapsack (20/50 items)
Empirical Analysis & Interpretation
Provides empirical trade-off visualizations across swarm, evolutionary, and local-search paradigms.
What This System Contributes
- 01Implemented the optimization algorithms, experimental harness, and comparative visualization suite.
Boundary Conditions & Future Work
- ·Fixed iteration/population budgets (50×50) designed for controlled algorithmic comparison.