1. Probability-based Evolutionary Algorithms
《中国科学 ：信息科学 》：人工智能领域青年学者研讨会
Probability-based Evolutionary Algorithms
Wei-Neng Chen （陈伟能）
School of Computer Science and Engineering
South China University of Technology
08/04/2017，昆明

2. Outline 1. Probability-Based Evolutionary Algorithms 2. Applications
3. Conclusions

3. Background Many real world problems are complex optimization problems
Do not have an accurate analytical model
Have many local optima
NP-Hard
Traditional optimization methods cannot be used in large-scale instances .
Multimodal
Routing & Logistics

4. Evolutionary Computation
Population-based stochastic algorithms which simulate intelligent behaviors
Popular Algorithms
Genetic algorithms (GA)
Differential evolution (DE)
Particle swarm optimization (PSO)
Ant colony optimization (ACO) …
Advantages
Do not make any assumption about the underlying fitness landscape
Find approximated solutions within acceptable time
Begin
Randomly initialize population & evaluation
Population Evolution
Evaluation of new solutions
Generation of a new population N
End? Y
Output

5. Challenges in Evolutionary Computation
Stability
Maintain good search diversity to avoid being trapped in local optima
Efficiency
Get satisfactory solutions within acceptable time
Generality
Be applicable for both continuous and discrete variables
Manage to handle different kinds of constraints
Velocity Update
Position Update
Self Cognition
Social interaction
Inertia
Particle Swarm Optimization (Kennedy & Eberhart, 1995)
Position
Velocity
are all real vectors
Limited in Continuous Space
Premature Convergence

6. Estimation of Distribution Algorithms
EDA (Lozano et al., 2002): building and sampling explicit probabilistic models of promising candidate solutions
Begin
Randomly initialize population & evaluation
f(x) x
Select promising candidate solutions
Build a probability model to estimate the distribution of candidate solution
Build a probability model to estimate the distribution of population
Sample new individuals from the model
EDA: focus on the global fitness landscape
Generate a new population
Advantages
Good search diversity
Applicable in continuous and discrete domains
Easy to understand
End? Y
Output

7. Probability-Based EC Frameworks
Inspired by EDA, two probability-based EC frameworks are built to address the challenges of EC algorithms
Challenge
Framework
A suitable probability distribution can bring in good search diversity
Stability:
How to maintain good search diversity and avoid premature convergence
Probability-Based EC for Multimodal Optimization
Generality:
How to be applicable for both continuous and discrete decision variables
Probability-Based EC for Mixed-Variable Optimization
Continuous / Discrete probability distribution

8. Probability-Based EC for Multimodal Optimization
Combine probability distribution (PD) estimation with niching
Different methods (EDA/ACO) can be used to build the PD
Results: significantly increase the detect rate of peaks
f f20
MOMMOP
22.5%
12.5%
M-EDA
45.8%
25.0%
M-ACO
50.2%
34.8%
Multimodal Estimation of Distribution Algorithms, IEEE Cyb, 2017
Adaptive multimodal ant colony optimization, IEEE TeVC, 2017

9. Probability-Based EC for Mixed-Variable Optimization
distribution
Pheromone Deposition
ACO in discrete space
ACO in continuous space
Adaptive multimodal ant colony optimization, IEEE TeVC, 2017 1 4 2 3
0.5
0.6
0.7
Position
Velocity
are all real vectors
Probability
distribution
PSO in continuous space
PSO in discrete space
A novel set-based particle swarm optimization method for discrete optimization problems, IEEE TeVC, 2010

10. Set-based Particle Swarm Optimization
Redefine the operators in PSO in discrete set space
陈伟能，华南理工大学

11. Advantages of S-PSO
Different PSO variants can be extended to their discrete versions
Global version PSO
S-GPSO
IEEE TEvC, 2010
Comprehensive learning PSO
S-CLPSO
Locally informed PSO
S-LIPS
GECCO, 2013
GECCO, 2015
IEEE Tcyb, 2017
MO PSO / Decomposition
S-MOPSO/D
Broad Applications
Traveling salesman problem / multiple knapsack problem (2010)
Vehicle routing (Gong et al., 2012, IEEE TSMC-C)
Coverage array generation (Wu et al., 2015 , IEEE TEvC)
Carpool service problem (Chou et al., 2016, IEEE Tcyb)
Logistics dispatching (Jia et al., in press, IEEE TSMC-S) ……
陈伟能，华南理工大学

12. Outline 1. Probability-Based Evolutionary Algorithms 2. Applications
3. Conclusions

13. Applications —— Resource Allocation
Workflow Application
task
Cloud Environment
Computational resource
Network Embedding
IEEE TSMC-C, 2009
IEEE SMC, 2017
task
mapping
task
task
task
task
task
task
Software project management
IEEE TSMC-C, 2010; IEEE TSE, 2013

14. Applications —— Graph/Network-Based Search
Detection of Overlapped Social Network Communities
A Maximal Clique Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection, IEEE TeVC, 2017

15. Applications —— Graph/Network-Based Search
Vehicle Routing
Set-based Representation
with a decoding scheme
Provide new best-known results on 29 benchmark instances
Optimizing the Vehicle Routing Problem With Time Windows: A Discrete Particle Swarm Optimization Approach, IEEE TSMC-C, 2012

16. Applications —— Graph/Network-Based Search
Dynamic vehicle routing
Dynamic optimization
Archive strategy to store historical results and accelerate convergence
Region partition to cut a large-scale problem into small pieces
A Dynamic Logistic Dispatching System With Set-Based Particle Swarm Optimization, IEEE TSMC-Systems, in press
Architecture of the dispatching system

17. Outline 1. Probability-Based Evolutionary Algorithms
2. EC for Smart Logistics Systems
3. Conclusions

18. Conclusions
Evolutionary computation (EC) has been one of the most important techniques for solving complex problems
Facing the two challenges of EC, two probability-based EC frameworks were built
Stability: Probability-based EC for multimodal optimization
Generality: Probability-based EC for mixed-variable optimization
The evolutionary algorithms have been applied to resource allocation and graph-based search problems
Challenge: curse of dimensionality / distributed management / dynamic & uncertainty environment

19. Thanks for your attention!