Metaheuristic Algorithms

Exploring evolutionary and swarm intelligence optimization methods

Metaheuristic Algorithms  

Introduction  

Metaheuristic algorithms are high-level problem-independent algorithmic frameworks that provide guidelines for developing heuristic optimization algorithms.

Evolutionary Algorithms  

Genetic Algorithm (GA)  

Inspired by natural evolution:

1. Selection: Choose parents based on fitness
2. Crossover: Combine genetic material
3. Mutation: Introduce random changes
4. Replacement: Form new population

Differential Evolution (DE)  

Uses vector differences for generating new candidate solutions.

Swarm Intelligence  

Particle Swarm Optimization (PSO)  

Simulates social behavior of bird flocking:

v_i(t+1) = w*v_i(t) + c1*r1*(pbest_i - x_i(t)) + c2*r2*(gbest - x_i(t))
x_i(t+1) = x_i(t) + v_i(t+1)

Ant Colony Optimization (ACO)  

Models foraging behavior of ants using pheromone trails.

Physics-Inspired Algorithms  

Simulated Annealing (SA)  

Based on the annealing process in metallurgy.

Gravitational Search Algorithm (GSA)  

Uses Newton’s law of universal gravitation.

Applications  

• Engineering design optimization
• Scheduling problems
• Machine learning hyperparameter tuning
• Combinatorial optimization problems