Issues with performance measures for dynamic multi-objective optimisation

In recent years a number of algorithms were proposed to solve dynamic multi-objective optimisation problems. However, a major problem in the field of dynamic multi-objective optimisation is a lack of standard performance measures to quantify the quality of solutions found by an algorithm. In addition, the selection of performance measures may lead to misleading results. This paper highlights issues that may cause misleading results when comparing dynamic multi-objective optimisation algorithms with performance measures that are currently used in the field.

[1]  Maoguo Gong,et al.  Clonal Selection Algorithm for Dynamic Multiobjective Optimization , 2005, CIS.

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

[3]  Joshua D. Knowles Local-search and hybrid evolutionary algorithms for Pareto optimization , 2002 .

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

[5]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[6]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[7]  Zikrija Avdagic,et al.  Evolutionary Approach to Solving Non-stationary Dynamic Multi-Objective Problems , 2009, Foundations of Computational Intelligence.

[8]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[9]  Xiaodong Li,et al.  On performance metrics and particle swarm methods for dynamic multiobjective optimization problems , 2007, 2007 IEEE Congress on Evolutionary Computation.

[10]  Andries Petrus Engelbrecht,et al.  Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[11]  Carlos A. Coello Coello,et al.  Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[12]  Andries Petrus Engelbrecht,et al.  Dynamic Multi-objective Optimisation Using PSO , 2010, Multi-Objective Swarm Intelligent System.

[13]  Günter Rudolph,et al.  Evolutionary Optimization of Dynamic Multiobjective Functions , 2006 .

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

[15]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

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

[17]  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.

[18]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[19]  David Wallace,et al.  Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach , 2006, GECCO '06.

[20]  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).

[21]  Julio Ortega Lopera,et al.  Parallel Processing for Multi-objective Optimization in Dynamic Environments , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[22]  Shengxiang Yang,et al.  Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments , 2014 .

[23]  Andries Petrus Engelbrecht,et al.  Analyses of guide update approaches for vector evaluated particle swarm optimisation on dynamic multi-objective optimisation problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[24]  A. Dickson On Evolution , 1884, Science.

[25]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[26]  Kalyanmoy Deb,et al.  Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications , 2003, EMO.

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

[28]  Xiaodong Li,et al.  Better Spread and Convergence: Particle Swarm Multiobjective Optimization Using the Maximin Fitness Function , 2004, GECCO.

[29]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[30]  Patrick M. Reed,et al.  Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design , 2005 .

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

[32]  Mario Cámara Sola,et al.  Parallel processing for dynamic multi-objetive optimization , 2010 .

[33]  Maximino Salazar Lechuga,et al.  Multi-objective optimisation using sharing in swarm optimisation algorithms , 2009 .

[34]  Karsten Weicker,et al.  Performance Measures for Dynamic Environments , 2002, PPSN.

[35]  Yuping Wang,et al.  An evolutionary algorithm for dynamic multi-objective optimization , 2008, Appl. Math. Comput..

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

[37]  Andries Petrus Engelbrecht,et al.  Hybridizing PSO and DE for improved vector evaluated multi-objective optimization , 2009, 2009 IEEE Congress on Evolutionary Computation.

[38]  Bin Li,et al.  Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization , 2010, Memetic Comput..

[39]  Rajkumar Buyya,et al.  A pareto following variation operator for fast-converging multiobjective evolutionary algorithms , 2008, GECCO '08.

[40]  Kay Chen Tan,et al.  A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization , 2009, IEEE Transactions on Evolutionary Computation.