Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power
Abstract
:1. Introduction
- It first proposes parallel heterogeneous model based on PSO and GWO.
- It introduces four new communication strategies to improve the abilities of exploration and exploitation.
- It dynamically changes the members of subgroup from the diversity of the population.
2. Preliminaries
2.1. Particle Swarm Optimization
2.2. Grey Wolf Optimizer
2.3. Population-Based Parallelization
2.3.1. Communication Models
- Star model
- Migration model
- Diffusion model
- Hybrid model
2.3.2. Communication Strategies
- Parameters with loosely correlated
- Parameters with strongly correlated
- Parameters with unknown correlation (Hybrid)
3. Novel Parallel Heterogeneous Algorithm
3.1. The Model of Parallel Heterogeneous Algorithm
3.2. New Communication Strategies
3.2.1. Communication Strategy with Ranking
3.2.2. Communication Strategy with Combination
Algorithm 1 Combination |
//ngroups.number is the number of subgroups |
for g = 1 : ngroups.number do |
//ngroups.algorithms is the number of meta-heuristics |
for j = 1 : ngroups.algorithms do |
if j == groups(g).algorithm then |
//ngroups.size is the number of subgroup |
for l = 1 : ngroups.size do |
temp(j,t(j)) = groups(g).pop(l); |
t(j) = t(j) + 1; |
end for |
end if |
end for |
end for |
for j = 1 : ngroups.algorithms do |
i = 1; |
temp(j) = SortPopulationByFitness(temp(j)); |
for g = 1 : ngroups.number do |
if j == groups(g).algorithm && i ≤ t(j) then |
groups(g).pop(p(g)) = temp(j,i); |
i = i + 1; |
p(g) = p(g) + 1; |
end if |
end for |
end for |
3.2.3. Communication Strategy with Dynamic Change
Algorithm 2 Dynamic Change |
Sort A //A is the best solutions of the subgroups |
Sort B // B is the virtual group |
for i = 1 : length(A), j = 1 : length(B) do |
if f(A(i)) < B(j) then |
B(j) = A(i); |
++i; |
else |
++j; |
end if |
end for |
3.2.4. Hybrid Communication Strategy
4. Experimental Results and Analysis
4.1. Parameters Configuration
4.2. Unimodal Functions
4.3. Multimodal Functions
4.4. Fixed-Dimension Multimodal Functions
4.5. Composite Multimodal Functions
5. Application for Wind Power Forecasting
5.1. The Model of Wind Power Forecasting Based on Hybrid Neural Network
5.2. Simulation Results
5.2.1. Data Preprocessing
5.2.2. The Evaluation Performance of Hybrid Model
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Function | Space | Dim | fmin |
---|---|---|---|
[−100, 100] | 30 | 0 | |
[−10, 10] | 30 | 0 | |
[−100, 100] | 30 | 0 | |
[−100, 100] | 30 | 0 | |
[−100, 100] | 30 | 0 | |
[−1.28, 1.28] | 30 | 0 |
Function | Space | Dim | fmin |
---|---|---|---|
[−500, 500] | 30 | −12,569 | |
[−5.12, 5.12] | 30 | 0 | |
[−32, 32] | 30 | 0 | |
[−600, 600] | 30 | 0 | |
[−50, 50] | 30 | 0 | |
[−50, 50] | 30 | 0 |
Function | Space | Dim | fmin |
---|---|---|---|
[−65, 65] | 2 | 1 | |
[−5, 5] | 4 | 0.00030 | |
[−5, 5] | 2 | −1.0316 | |
[−5, 5] | 2 | 0.398 | |
[−2, 2] | 2 | 3 | |
[1, 3] | 3 | −3.86 | |
[0, 1] | 6 | −3.32 | |
[0, 10] | 4 | −10.1532 | |
[0, 10] | 4 | −10.4028 | |
[0, 10] | 4 | −10.5363 |
Function | Space | Dim | fmin |
---|---|---|---|
[−5, 5] | 30 | 0 | |
[−5, 5] | 30 | 0 | |
[−5, 5] | 30 | 0 | |
[−5, 5] | 30 | 0 | |
[−5, 5] | 30 | 0 | |
[−5, 5] | 30 | 0 |
Algorithm | Communication Strategy | Main Parameters Setting |
---|---|---|
PH-R | Ranking | |
PH-C | Combination | |
PH-D | Dynamic Change | |
PH-H | Hybrid of Ranking and Combination |
Function | PGWO | PH-R | PH-C | PH-D | PH-H | |||||
---|---|---|---|---|---|---|---|---|---|---|
AVG | STSD | AVG | STSD | AVG | STSD | AVG | STSD | AVG | STSD | |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
0 | 0 | 0 | ||||||||
Algorithm | Accuracy (%) |
---|---|
PH-R | 84.97 |
PH-C | 83.89 |
PH-D | 84.49 |
PH-H | 83.67 |
NN | 73.30 |
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Pan, J.-S.; Hu, P.; Chu, S.-C. Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power. Processes 2019, 7, 845. https://doi.org/10.3390/pr7110845
Pan J-S, Hu P, Chu S-C. Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power. Processes. 2019; 7(11):845. https://doi.org/10.3390/pr7110845
Chicago/Turabian StylePan, Jeng-Shyang, Pei Hu, and Shu-Chuan Chu. 2019. "Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power" Processes 7, no. 11: 845. https://doi.org/10.3390/pr7110845