Combining mutual information and stable matching strategy for dynamic evolutionary multi-objective optimization

ABSTRACT It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions; (2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively; and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.

[1]  Hussein A. Abbass,et al.  A Benchmark Test Suite for Dynamic Evolutionary Multiobjective Optimization , 2017, IEEE Transactions on Cybernetics.

[2]  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).

[3]  Carlos A. Coello Coello,et al.  Evolutionary multiobjetive optimization in non-stationary environments , 2005 .

[4]  Elizabeth F. Wanner,et al.  A dynamic multiobjective hybrid approach for designing Wireless Sensor Networks , 2009, 2009 IEEE Congress on Evolutionary Computation.

[5]  Qingfu Zhang,et al.  Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization , 2007, EMO.

[6]  Aluizio F. R. Araújo,et al.  Generalized immigration schemes for dynamic evolutionary multiobjective optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[7]  David Wallace,et al.  Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach , 2006, GECCO.

[8]  Julio Ortega Lopera,et al.  A single front genetic algorithm for parallel multi-objective optimization in dynamic environments , 2009, Neurocomputing.

[9]  Kalyanmoy Deb,et al.  Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling , 2007, EMO.

[10]  Robert W. Irving,et al.  The Stable marriage problem - structure and algorithms , 1989, Foundations of computing series.

[11]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[12]  Kay Chen Tan,et al.  A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment , 2010, Memetic Comput..

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

[14]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[16]  Kay Chen Tan,et al.  Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction , 2016, IEEE Transactions on Cybernetics.

[17]  Julio Ortega Lopera,et al.  Approaching Dynamic Multi-Objective Optimization Problems by Using Parallel Evolutionary Algorithms , 2010, Advances in Multi-Objective Nature Inspired Computing.

[18]  Demin Xu,et al.  UAV online path planning based on dynamic multiobjective evolutionary algorithm , 2011, Proceedings of the 30th Chinese Control Conference.

[19]  Xin Yao,et al.  Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art , 2013 .

[20]  Zhuhong Zhang,et al.  Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control , 2008, Appl. Soft Comput..

[21]  P. Di Barba.  Dynamic Multiobjective Optimization: A Way to the Shape Design With Transient Magnetic Fields , 2008 .

[22]  Xin Yao,et al.  A framework for finding robust optimal solutions over time , 2013, Memetic Comput..

[23]  Ponnuthurai N. Suganthan,et al.  Evolutionary multiobjective optimization in dynamic environments: A set of novel benchmark functions , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[24]  Fang Liu,et al.  A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization , 2010, GECCO '10.

[25]  Yuping Wang,et al.  New Evolutionary Algorithm for Dynamic Multiobjective Optimization Problems , 2006, ICNC.

[26]  Mark Johnston,et al.  Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming , 2014, IEEE Transactions on Evolutionary Computation.

[27]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[28]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[29]  Kalyanmoy Deb,et al.  Dynamic multiobjective optimization problems: test cases, approximations, and applications , 2004, IEEE Transactions on Evolutionary Computation.