Key challenges and future directions of dynamic multi-objective optimisation

Many real-world problems have more than one objective and are dynamic in nature, where either an objective function or constraint can vary over time. These problems are referred to as dynamic multi-objective optimisation problems (DMOOPs). A key challenge for dynamic multi-objective optimisation (DMOO) research is efficiently evaluating and analysing the performance of DMOO algorithms (DMOAs). This includes benchmarks, performance measures and the approach used to analyse the obtained results. Most research in recent years focussed on either dynamic single-objective or static multi-objective optimisation. In the field of DMOO, research focussed on unconstrained DMOOPs. A few papers have recently proposed constrained DMOOPs. Therefore, a key sub-challenge in DMOO is to have a standard benchmark suite that contains both unconstrained and constrained DMOOPs with various characteristics. In addition, the constraints used in the benchmarks should be guided by constraints that occur in real-world problems. Most approaches used to analyse the performance of DMOAs do not take into account how well a DMOA tracks the changing optimal solutions over time, i.e. how well it performs in each of the various environments. Furthermore, there are still certain DMOOPs that the proposed algorithms struggle to solve. Therefore, more research is required with regards to the development of algorithms that can solve DMOOPs efficiently. Another important aspect of DMOO is the decision making process that can either occur offline or interactively. This paper discusses these key challenges and progress that has been made to address these challenges. Furthermore, actions to deal with the outstanding issues are also proposed.

[1]  Jingxuan Wei,et al.  A novel particle swarm optimization algorithm with local search for dynamic constrained multi-objective optimization problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[2]  Knowledge Incorporation in Evolutionary Computation [Book Review] , 2006, IEEE Computational Intelligence Magazine.

[3]  Tianyou Chai,et al.  A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing , 2013, Appl. Soft Comput..

[4]  Xin Yao,et al.  Continuous Dynamic Constrained Optimization—The Challenges , 2012, IEEE Transactions on Evolutionary Computation.

[5]  Andries P. Engelbrecht,et al.  Analysing the performance of dynamic multi-objective optimisation algorithms , 2013, 2013 IEEE Congress on Evolutionary Computation.

[6]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[7]  Demin Xu,et al.  Intelligent Online Path Planning for UAVs in Adversarial Environments , 2012 .

[8]  Weiqi Li,et al.  A Parallel Procedure for Dynamic Multi-objective TSP , 2012, 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications.

[9]  Filip Logist,et al.  An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty , 2015, Expert Syst. Appl..

[10]  Andries Petrus Engelbrecht,et al.  Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[11]  Siamak Noori,et al.  A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application , 2014, Appl. Soft Comput..

[12]  Amos H. C. Ng,et al.  Multi-objective optimization and analysis of the inventory management model , 2014, SummerSim.

[13]  W. Du,et al.  Multi-objective differential evolution with ranking-based mutation operator and its application in chemical process optimization , 2014 .

[14]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[15]  Zhou-quan Du,et al.  Downlink power allocation in distributed satellite system based on dynamic multi-objective optimization , 2015, International Conference on Wireless Communications and Signal Processing.

[16]  Alireza Soroudi,et al.  Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty , 2012 .

[17]  Jan Van Impe,et al.  Robust multi-objective dynamic optimization of chemical processes using the Sigma Point method , 2016 .

[18]  Bin Chen,et al.  Dynamic optimal control of sustained overvoltage during power system restoration process , 2015, 2015 IEEE Power & Energy Society General Meeting.

[19]  Hisao Ishibuchi,et al.  Evolutionary many-objective optimization , 2008, 2008 3rd International Workshop on Genetic and Evolving Systems.

[20]  Rajkumar Roy,et al.  Dynamic multi-objective optimisation for machining gradient materials , 2008 .

[21]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[22]  Andries Petrus Engelbrecht,et al.  Performance measures for dynamic multi-objective optimisation algorithms , 2013, Inf. Sci..

[23]  Andries Petrus Engelbrecht,et al.  Issues with performance measures for dynamic multi-objective optimisation , 2013, 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE).

[24]  Yuping Wang,et al.  Hyper rectangle search based particle swarm algorithm for dynamic constrained multi-objective optimization problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[25]  Andries Petrus Engelbrecht,et al.  Benchmarks for dynamic multi-objective optimisation algorithms , 2014, CSUR.

[26]  Marde Helbig,et al.  Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation , 2012 .

[27]  Andries P. Engelbrecht,et al.  Challenges of Dynamic Multi-objective Optimisation , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

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

[29]  Shengxiang Yang,et al.  A framework of scalable dynamic test problems for dynamic multi-objective optimization , 2014, 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE).

[30]  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.

[31]  António Gomes Correia,et al.  An evolutionary multi-objective optimization system for earthworks , 2015, Expert Syst. Appl..

[32]  Rami Bahsoon,et al.  Dynamic QoS Optimization Architecture for Cloud-Based DDDAS , 2013, ICCS.

[33]  Xin Yao,et al.  Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems , 2015, Inf. Sci..

[34]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[35]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[36]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach , 2014, IEEE Transactions on Evolutionary Computation.

[37]  Tianyou Chai,et al.  Multiobjective optimization for planning of mineral processing under varied equipment capability , 2013, Proceedings of the 2013 International Conference on Advanced Mechatronic Systems.

[38]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[39]  Andries Petrus Engelbrecht,et al.  Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems , 2014, Swarm Evol. Comput..

[40]  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).