metaheuristics-tutorial

Please do not refer to this code until Nov. 2019.

metaheuristics-tutorial is a tutorial for who is studying metaheuristics, combinatorial optimization, operations research using Python, DEAP (Distributed Evolutionary Algorithms in Python).

Dependencies

metaheuristics-tutorial requires:

• Python (>= 3.7.4)
• NumPy (>= 1.16.5)
• pandas (>= 0.25.1)
• SciPy (>= 0.17.0)
• PuLP (>= 1.6.9)
• DEAP (>= 1.3.1)
• tabulate (>= 0.8.5)

metaheuristics-tutoral does not support Python 2.7.

Combinatorial Optimization using Metaheuristics

Problems

1. Travelling Salesman Problem
2. Knapsack Problem

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Simulated Annealing (SA)

1. Description
2. Materials
   • Paper -
3. Continuous -
4. Discrete -

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Tabu Search (TS)

1. Description
2. Materials
   • Paper -
3. Discrete

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Genetic Algorithm (GA)

1. Materials
   • Paper -
2. Continuous -
3. Discrete -

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Evolution Strategy (ES)

1. Materials
   • Paper -
2. Continuous -

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Particle Swarm Optimization (PSO)

1. Materials
   • Paper -
2. Continuous -
3. Discrete -

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Ant Colony Optimization (ACO)

1. Materials
   • Paper -
2. Discrete -

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Changelog