Optimization is the process of finding the best possible solution to a problem within a defined set of constraints and objectives. It plays a critical role in various fields, from engineering and computer science to finance and logistics. By maximizing or minimizing an objective function, optimization ensures efficient resource utilization, cost reduction, and enhanced performance.
- Efficiency: Helps in solving complex real-world problems efficiently.
- Resource Management: Ensures optimal use of limited resources.
- Scalability: Facilitates handling large-scale problems.
- Gradient-Based Optimization: Relies on the gradient of the objective function for optimization (e.g., Gradient Descent).
- Evolutionary Algorithms: Mimics natural evolution (e.g., Genetic Algorithm, Differential Evolution).
- Swarm Intelligence: Inspired by collective behavior of social creatures (e.g., Particle Swarm Optimization, Ant Colony Optimization).
- Metaheuristic Optimization: Provides approximate solutions for complex problems (e.g., Simulated Annealing, Harmony Search).
Below is a list of algorithms implemented in this repository:
- Gradient Descent: An iterative algorithm for minimizing functions by following the gradient.
- Newton's Method: Utilizes the second derivative to find local minima or maxima.
- Stochastic Gradient Descent: A variant of Gradient Descent that uses random samples for optimization.
- Nelder-Mead Method: A simplex-based optimization technique.
- Best-First Search: Explores a graph to find the optimal solution.
- Hill Climbing: A greedy algorithm that iteratively moves toward the optimal solution.
- Tree-Structured Parzen Estimator: A Bayesian optimization technique.
- African Vultures Optimization Algorithm: Inspired by the scavenging behavior of vultures.
- Ant Colony Optimization: Mimics the behavior of ants searching for food.
- Bat Algorithm: Inspired by echolocation behavior of bats.
- Bees Algorithm: Models the foraging behavior of bees.
- Differential Evolution: A population-based optimization technique.
- Firefly Algorithm: Inspired by the flashing behavior of fireflies.
- Genetic Algorithm: Mimics natural selection to evolve solutions.
- Particle Swarm Optimization: Inspired by the social behavior of birds and fish.
- Simulated Annealing: Mimics the annealing process in metallurgy.
- Harmony Search: Inspired by musical improvisation.
- Imperialist Competitive Algorithm: Simulates imperialistic competition.
- Whale Optimization Algorithm: Mimics the hunting behavior of humpback whales.
- Grey Wolf Optimizer: Inspired by the leadership hierarchy of grey wolves.
- Brain Storm Optimization: Mimics the brainstorming process for creative solutions.
- Cuckoo Search: Inspired by the brood parasitism behavior of cuckoo birds.
- Biogeography-Based Optimization: Models the migration patterns of species.
- Teaching-Learning-Based Optimization: Mimics the teaching-learning process in a classroom.
- Galaxy Gravity Optimization: Inspired by the gravitational interactions of galaxies.
- Victoria Amazonica Optimization Algorithm: Inspired by the growth and spread of the Victoria Amazonica plant.
- Weevil Damage Optimization Algorithm: Simulates the damage behavior of weevils.
- MOEA/D: A multi-objective evolutionary algorithm based on decomposition.
- NSGA-II: A fast and elitist multi-objective genetic algorithm.
- Tabu Search: Uses a tabu list to avoid cycling in search processes.
- Ant Colony Optimization Bin Packing: Specialized for solving bin packing problems.
- Firefly Space-Time Bending: A variant of Firefly Algorithm applied to space-time optimization.
- Simulated Annealing Quadratic Assignment Problem: Special implementation for QAP.
The following problems have been solved using the above algorithms:
- Bin Packing Problem: Solved using Ant Colony Optimization.
- Feature Selection: Achieved with the Bat Algorithm.
- Economic Dispatching: Handled using Bees Algorithm.
- Minimum Spanning Tree: Optimized with Biogeography-Based Optimization.
- Parallel Machine Scheduling: Solved using Brain Storm Optimization.
- Traveling Salesman Problem: Addressed with Cuckoo Search.
- Protein Structure Prediction: Solved using Differential Evolution.
- Image Segmentation: Implemented with Firefly Algorithm.
- Evolutionary Art: Created using Genetic Algorithm and Particle Swarm Optimization.
- Latent Space Optimization: Conducted with Grey Wolf Optimizer for VAE.
- Quadratic Assignment Problem: Solved using Simulated Annealing.
- Resource Allocation: Implemented with Stochastic Gradient Descent.
- Vehicle Routing Problem: Solved using Teaching-Learning-Based Optimization.
- Hub Location Allocation: Addressed with Whale Optimization Algorithm.
- Space-Time Bending Optimization: Solved using Firefly Algorithm.
- Regression Analysis: Conducted using Harmony Search.
- Evolved Antenna Design: Implemented with Particle Swarm Optimization.
- Exoplanetary Adaptation: Solved with Genetic Algorithm.
- Traveling Salesman Problem (Cuckoo Search): Addressed using Cuckoo Search Algorithm.
- Clustering Problem (Differential Evolution): Solved using Differential Evolution.
- Optimized Feature Selection (Bat Algorithm): Utilized for feature selection optimization.
- Protein Folding Prediction (Differential Evolution): Addressed with advanced Differential Evolution techniques.
Each algorithm is implemented in a separate .py file for ease of use and modularity.
The problems are categorized and solved using specific algorithms, with clear explanations in the comments.
Detailed presentations explaining the theoretical and practical aspects of each algorithm and problem are included.
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.