Table 4 The induction and evaluation of DRE uncertainty planning solution algorithms

Traditional algorithm

Sequential least squares

[125]

Minimize the sum of error squares

Iterative bi-layer optimization algorithm

[127, 139]

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