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

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

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

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

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

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

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

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 f19 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

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

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

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

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

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

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

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

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

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

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

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