Neural Network-Based Information Transfer for Dynamic Optimization

In dynamic optimization problems (DOPs), as the environment changes through time, the optima also dynamically change. How to adapt to the dynamic environment and quickly find the optima in all environments is a challenging issue in solving DOPs. Usually, a new environment is strongly relevant to its previous environment. If we know how it changes from the previous environment to the new one, then we can transfer the information of the previous environment, e.g., past solutions, to get new promising information of the new environment, e.g., new high-quality solutions. Thus, in this paper, we propose a neural network (NN)-based information transfer method, named NNIT, to learn the transfer model of environment changes by NN and then use the learned model to reuse the past solutions. When the environment changes, NNIT first collects the solutions from both the previous environment and the new environment and then uses an NN to learn the transfer model from these solutions. After that, the NN is used to transfer the past solutions to new promising solutions for assisting the optimization in the new environment. The proposed NNIT can be incorporated into population-based evolutionary algorithms (EAs) to solve DOPs. Several typical state-of-the-art EAs for DOPs are selected for comprehensive study and evaluated using the widely used moving peaks benchmark. The experimental results show that the proposed NNIT is promising and can accelerate algorithm convergence.

[1]  Carlos Cruz,et al.  Optimization in dynamic environments: a survey on problems, methods and measures , 2011, Soft Comput..

[2]  Changhe Li,et al.  A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments , 2012, IEEE Transactions on Evolutionary Computation.

[3]  Shengxiang Yang,et al.  Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments , 2008, Evolutionary Computation.

[4]  Gary G. Yen,et al.  Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms , 2016, IEEE Transactions on Evolutionary Computation.

[5]  Shengxiang Yang,et al.  Particle Swarm Optimization With Composite Particles in Dynamic Environments , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Mohammad Reza Meybodi,et al.  novel multi-swarm algorithm for optimization in dynamic environments based n particle swarm optimization , 2013 .

[7]  Jun Zhang,et al.  Neural Network for Change Direction Prediction in Dynamic Optimization , 2018, IEEE Access.

[8]  Jun Zhang,et al.  An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing , 2018, IEEE Transactions on Evolutionary Computation.

[9]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[10]  Jun Zhang,et al.  Dual-Strategy Differential Evolution With Affinity Propagation Clustering for Multimodal Optimization Problems , 2018, IEEE Transactions on Evolutionary Computation.

[11]  Jun Zhang,et al.  Cloudde: A Heterogeneous Differential Evolution Algorithm and Its Distributed Cloud Version , 2017, IEEE Transactions on Parallel and Distributed Systems.

[12]  Shengxiang Yang,et al.  Memory-based immigrants for genetic algorithms in dynamic environments , 2005, GECCO '05.

[13]  Farzaneh Abdollahi,et al.  Adaptive Consensus Control of Nonlinear Multiagent Systems With Unknown Control Directions Under Stochastic Topologies , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Na Wang,et al.  Multi-swarm optimization algorithm for dynamic optimization problems using forking , 2008, 2008 Chinese Control and Decision Conference.

[15]  Ming Yang,et al.  An Adaptive Multipopulation Framework for Locating and Tracking Multiple Optima , 2016, IEEE Transactions on Evolutionary Computation.

[16]  Xiaodong Li,et al.  Comparing particle swarms for tracking extrema in dynamic environments , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[17]  Tianlong Gu,et al.  Historical and Heuristic-Based Adaptive Differential Evolution , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Wenjian Luo,et al.  Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in Dynamic Environments , 2009, ISICA.

[19]  Andries Petrus Engelbrecht,et al.  Differential evolution for dynamic environments with unknown numbers of optima , 2013, J. Glob. Optim..

[20]  Changhe Li,et al.  A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments , 2010, IEEE Transactions on Evolutionary Computation.

[21]  Claudio Rossi,et al.  Tracking Moving Optima Using Kalman-Based Predictions , 2008, Evolutionary Computation.

[22]  Liang Zhao,et al.  Organizational Data Classification Based on the Importance Concept of Complex Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Jian-Xin Xu,et al.  Data-Driven Iterative Feedforward Tuning for a Wafer Stage: A High-Order Approach Based on Instrumental Variables , 2019, IEEE Transactions on Industrial Electronics.

[24]  Mohammed Bennamoun,et al.  Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Jun Zhang,et al.  Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach , 2019, IEEE Transactions on Cybernetics.

[26]  Jun Zhang,et al.  Automatic Niching Differential Evolution With Contour Prediction Approach for Multimodal Optimization Problems , 2020, IEEE Transactions on Evolutionary Computation.

[27]  Swagatam Das,et al.  An Adaptive Differential Evolution Algorithm for Global Optimization in Dynamic Environments , 2014, IEEE Transactions on Cybernetics.

[28]  Gary G. Yen,et al.  Dynamic optimization using cultural based PSO , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[29]  Swagatam Das,et al.  A Cluster-Based Differential Evolution Algorithm With External Archive for Optimization in Dynamic Environments , 2013, IEEE Transactions on Cybernetics.

[30]  Xin Yu,et al.  A multi-point local search algorithm for continuous dynamic optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[31]  Hidehiro Nakano,et al.  An artificial bee colony algorithm with a memory scheme for dynamic optimization problems , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[32]  Lihua Yue,et al.  Combining multipopulation evolutionary algorithms with memory for dynamic optimization problems , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[33]  Qingfu Zhang,et al.  A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization , 2014, IEEE Transactions on Cybernetics.

[34]  Adnan Khashman,et al.  Prototype-Incorporated Emotional Neural Network , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Gary G. Yen,et al.  Dynamic Evolutionary Algorithm With Variable Relocation , 2009, IEEE Transactions on Evolutionary Computation.

[36]  Dumitru Dumitrescu,et al.  Evolutionary swarm cooperative optimization in dynamic environments , 2009, Natural Computing.

[37]  Bin Jiang,et al.  A Micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy , 2016, Soft Computing.

[38]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[39]  Mohammad Reza Meybodi,et al.  mNAFSA: A novel approach for optimization in dynamic environments with global changes , 2014, Swarm Evol. Comput..

[40]  Jie Zhang,et al.  Coevolutionary Particle Swarm Optimization With Bottleneck Objective Learning Strategy for Many-Objective Optimization , 2019, IEEE Transactions on Evolutionary Computation.

[41]  Emma Hart,et al.  A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems , 1998, PPSN.

[42]  Salwani Abdullah,et al.  A multi-population harmony search algorithm with external archive for dynamic optimization problems , 2014, Inf. Sci..

[43]  Xin Yao,et al.  Population-Based Incremental Learning With Associative Memory for Dynamic Environments , 2008, IEEE Transactions on Evolutionary Computation.

[44]  Qingming Huang,et al.  Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[45]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[46]  Wen Yu,et al.  Data-Driven Fuzzy Modeling Using Restricted Boltzmann Machines and Probability Theory , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[47]  Isaac Chairez,et al.  Adaptive Unknown Input Estimation by Sliding Modes and Differential Neural Network Observer , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[48]  Andries Petrus Engelbrecht,et al.  Niching for Dynamic Environments Using Particle Swarm Optimization , 2006, SEAL.

[49]  Yi Liang,et al.  Dynamic optimization with an improved θ-PSO based on memory recall , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[50]  Phillip D. Stroud,et al.  Kalman-extended genetic algorithm for search in nonstationary environments with noisy fitness evaluations , 2001, IEEE Trans. Evol. Comput..

[51]  Peter J. Bentley,et al.  Dynamic Search With Charged Swarms , 2002, GECCO.

[52]  Shengxiang Yang,et al.  An adaptive local search algorithm for real-valued dynamic optimization , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[53]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[54]  Anabela Simões,et al.  Evolutionary Algorithms for Dynamic Environments: Prediction Using Linear Regression and Markov Chains , 2008, PPSN.

[55]  Hao Wu,et al.  A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty , 2017, Complex..