Classification | Algorithm | References | Characteristic |
---|---|---|---|
Traditional algorithm | Sequential least squares | [125] | Minimize the sum of error squares |
 | Iterative bi-layer optimization algorithm | Nonlinear and non-convex function and constraints | |
 | Augmented epsilon constraint algorithm | [135] | The most used algorithm for multi-objective optimization |
 | Constraint-based iterative search algorithm | [137] | Based on maximum reliability and minimum cost, the optimal solution result is moderate |
Intelligent algorithm | Improved PSO algorithm based on map-reduce | [131] | Reduce the particle search scope of a single evolutionary algorithm |
 | Multi-objective PSO algorithm | [134] | Use random selection and adaptive grid method |
 | Strength Pareto evolutionary algorithm 2 | [136] | Use a set of chromosome number chain solutions. Higher fitness value |
 | Improve teaching optimization algorithm | [138] | Enhances the performance of the solution algorithm in global search |
 | Improved PSO algorithm | [142] | Overcome the inherent trend of local traps in particle swarm optimization |
Hybrid algorithm | Column generation and sharing algorithm | [124] | Reduce the computational burden of the long-term planning uncertainty model |
 | Hybrid big bang-big collision algorithm | [128] | Higher precision in the optimization performance of the high-dimensional function |
 | Algorithm based on consensus and gradient strategy | [129] | It's proved that the distributed energy coordination problem can be modified into a convex equivalence problem |
 | Quasi-opposite chaos selfish herd optimization algorithm | [130] | Combine the chaotic linear search and quasi-oppositional learning to have a faster solution |
 | Genetic algorithm | [140] | Higher precision of optimal solution |
 | Harmony search algorithm and firefly algorithm combination | [141] | High quality, good convergence characteristic and less iterative process |