A First-Order Difference Model-Based Evolutionary Dynamic Multiobjective Optimization

This paper presents a novel algorithm to solve dynamic multiobjective optimization problems. In dynamic multiobjective optimization problems, multiple objective functions and/or constraints may change over time, which requires a multiobjective optimization algorithm to track the moving Pareto-optimal solutions and/or Pareto-optimal front. A first-order difference model is designed to predict the new locations of a certain number of Pareto-optimal solutions based on the previous locations when an environmental change is detected. In addition, a part of old Pareto-optimal solutions are retained to the new population. The prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition to solve the dynamic multiobjective optimization problems. In such a way, the changed POS or POF can be tracked more quickly. The proposed algorithm is tested on a number of typical benchmark problems with different dynamic characteristics and difficulties. Experimental results show that the proposed algorithm performs competitively when addressing dynamic multiobjective optimization problems in comparisons with the other state-of-the-art algorithms.

[1]  Bin Li,et al.  Investigation of memory-based multi-objective optimization evolutionary algorithm in dynamic environment , 2009, 2009 IEEE Congress on Evolutionary Computation.

[2]  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.

[3]  Lamjed Ben Said,et al.  Multi-objective Optimization with Dynamic Constraints and Objectives: New Challenges for Evolutionary Algorithms , 2015, GECCO.

[4]  Qingfu Zhang,et al.  Biased Multiobjective Optimization and Decomposition Algorithm , 2017, IEEE Transactions on Cybernetics.

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

[6]  Yaochu Jin,et al.  A directed search strategy for evolutionary dynamic multiobjective optimization , 2014, Soft Computing.

[7]  Liu Min,et al.  Memory Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition , 2013 .

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

[9]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[10]  Shengxiang Yang,et al.  Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons , 2017, IEEE Transactions on Cybernetics.

[11]  Shengxiang Yang,et al.  Evolutionary dynamic optimization: A survey of the state of the art , 2012, Swarm Evol. Comput..

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

[13]  Shengxiang Yang,et al.  A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization , 2017, IEEE Transactions on Evolutionary Computation.

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

[15]  Xiangxiang Zeng,et al.  A Stable Matching-Based Selection and Memory Enhanced MOEA/D for Evolutionary Dynamic Multiobjective Optimization , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[16]  Kay Chen Tan,et al.  Dynamic Multiobjective Optimization Using Evolutionary Algorithm with Kalman Filter , 2013 .

[17]  Min Liu,et al.  Memory Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition: Memory Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Decomposition , 2014 .

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

[19]  I. Hatzakis,et al.  Topology of Anticipatory Populations for Evolutionary Dynamic Multi-Objective Optimization , 2006 .