| Bio-inspired algorithms |
Genetic algorithm (GA) |
Simulates natural selection and genetic recombination based on Darwinian evolution |
1975 |
| Particle swarm optimization (PSO) |
Inspired by bird flocking behavior, where particles update their velocity and position based on individual and group best solutions |
1995 |
| Ant colony optimization (ACO) |
Models pheromone-based communication in ant colonies for cooperative pathfinding |
1992 |
| Mathematical theory-driven algorithms |
Weighted sum method |
Uses linear programming techniques to transform MOO into a single-objective problem |
1950s |
| MOEA/D |
Applies game theory and decomposition strategies to divide high-dimensional objectives into subproblems for cooperative solving |
2007 |
| Physics-inspired algorithms |
Simulated annealing (SA) |
Maps the annealing process in metallurgy, using a temperature-controlled probabilistic acceptance mechanism to avoid local optima |
1983 |
| Gravitational search algorithm (GSA) |
Based on Newton’s law of gravitation, simulating the attraction forces between solutions to guide convergence |
2009 |
| Machine learning-enhanced optimization |
Neural network-based surrogate models |
Uses deep learning to approximate objective functions, reducing computational costs for real-world optimization |
2010s |
| Reinforcement learning-based optimization |
Integrates Markov decision processes (MDP) and vectorized reward functions for optimizing strategies in dynamic environments |
1998 |
