Metaheuristic Algorithms

This repo contains the implementation in Python with a tutorial of these Metaheuristic Algorithms :

• Genetic Algorithm (GA)
• Tabo Search (TS)
• Simulated Anniling (SA)
• Iterated Local Search (ILS)
• Grey Wolf Optimization (GWO)
• Flower Pollination Algorithm (FPA)
• Shuffled Frog-Leaping Algorithm (SFLA)
• Pratical Swarm Optimization (PSO)
• Ant Colonny Optimization (ACO)

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Genetic Algorithm (GA)

• A Genetic Algorithm is a search heuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). It is used to generate solutions to optimization and search problems.

Tabu Search

• Tabu Search is a metaheuristic search method employing local search techniques and using memory structures to avoid cycles and ensure exploration of new areas in the search space.

Simulated Annealing (SA)

• Simulated Annealing is a probabilistic technique for approximating the global optimum of a given function. It is inspired by the annealing process in metallurgy.

Iterated Local Search (ILS)

• Iterated Local Search is an optimization algorithm that iteratively applies a local search to modified versions of the best solutions found so far.

Grey Wolf Optimization (GWO)

• Grey Wolf Optimization is a nature-inspired metaheuristic algorithm based on the social hierarchy and hunting behavior of grey wolves.

Flower Pollination Algorithm (FPA)

• The Flower Pollination Algorithm (FPA) is a nature-inspired optimization algorithm based on the pollination process of flowering plants. It mimics the behavior of flowers and pollinators to solve optimization problems.

Shuffled Frog-Leaping Algorithm (SFLA)

• The Shuffled Frog-Leaping Algorithm (SFLA) is a memetic metaheuristic optimization algorithm inspired by the social behavior of frogs in finding food. It combines the benefits of both local search and global information exchange.

Particle Swarm Optimization (PSO)

• Particle Swarm Optimization (PSO) is a population-based optimization algorithm inspired by the social behavior of birds flocking or fish schooling. It optimizes a problem by iteratively improving candidate solutions based on their own experiences and those of their neighbors.

Ant Colony Optimization (ACO)

• Ant Colony Optimization (ACO) is a probabilistic technique inspired by the foraging behavior of ants. It is used for solving combinatorial optimization problems by simulating the pheromone trail laying and following behavior of ants.