Biao Xu
ABSTRACT
Multi-objective optimization problems with changing variables are very common in real-world applications. This kind of problems often has a changing Pareto-optimal set and a complex relation among decision variables. In order to rapidly track the time-dependent Pareto-optimal front, we propose a framework of parallel cooperative co-evolution based on dynamically grouping decision variables. Decision variables are first divided into a number of groups using the Spearman rank correlation analysis, with different groups having a weak correlation. Then, a sub-population is employed to optimize decision variables in each group using a traditional multi-objective evolutionary algorithm. The evaluation of a complete solution is fulfilled through the cooperation among sub-populations. We compare the proposed algorithm with three state-of-the-art algorithms by applying them to two modified benchmark optimization problems. Empirical results show that the proposed algorithm is superior to the compared ones.
- Manuel Blanco Abello, Lam Thu Bui, and Zbignew Michalewicz. 2011. An adaptive approach for solving dynamic scheduling with time-varying number of tasks-Part II. In Evolutionary Computation (GEO), 2011 IEEE Congress on. 1711--1718.Google Scholar
Cross Ref
- Jürgen Branke, Thomas Kaußler, Christian Smidt, and Hartmut Schmeck. 2000. A multi-population approach to dynamic optimization problems. In Evolutionary Design and Manufacture. Springer, 299--307.Google Scholar
- Swagatam Das, Ankush Mandal, and Rohan Mukherjee. 2014. An adaptive differential evolution algorithm for global optimization in dynamic environments. IEEE Transactions on Cybernetics 44, 6 (2014), 966--978.Google ScholarCross Ref
- Kalyanmoy Deb. 1999. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation 7, 3 (1999), 205--230. Google ScholarDigital Library
- Kalyanmoy Deb, Bhaskara Rao N Udaya, and S. Karthik. 2007. Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. 4403 (2007), 803--817. Google ScholarDigital Library
- Bernabé Dorronsoro, GréGoire Danoy, Pascal Bouvry, and Antonio J. Nebro. 2011. Multi-objective Cooperative Coevolutionary Evolutionary Algorithms for Continuous and Combinatorial Optimization. Springer Berlin Heidelberg. 49--74 pages.Google Scholar
- Bernabé Dorronsoro, GréGoire Danoy, Antonio J Nebro, and Pascal Bouvry. 2013. Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution. Computers & Operations Research 40, 6 (2013), 1552--1563. Google ScholarDigital Library
- Marco Farina, Kalyanmoy Deb, and Paolo Amato. 2004. Dynamic multiobjective optimization problems: test cases, approximations, and applications. Evolutionary Computation, IEEE Transactions on 8, 5 (2004), 425--442. Google ScholarDigital Library
- Iason Hatzakis and David Wallace. 2006. Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, 1201--1208. Google ScholarDigital Library
- Yaochu Jin and Jürgen Branke. 2005. Evolutionary optimization in uncertain environments-a survey. Evolutionary Computation IEEE Transactions on 9, 3 (2005), 303--317. Google ScholarDigital Library
- Omprakash Kaiwartya, Sushil Kumar, DK Lobiyal, Pawan Kumar Tiwari, Abdul Hanan Abdullah, and Ahmed Nazar Hassan. 2015. Multiobjective dynamic vehicle routing problem and time seed based solution using particle swarm optimization. Journal of Sensors 2015 (2015).Google Scholar
- Ann Lehman, Norm O'Rourke, Larry Hatcher, and Ed Stepanski. 2005. JMP for Basic univariate and multivariate statistics: Methods for researchers and social scientists second edition. (2005). Google ScholarDigital Library
- Xiaodong Li and Xin Yao. 2012. Cooperatively Coevolving Particle Swarms for Large Scale Optimization. IEEE Transactions on Evolutionary Computation 16, 2 (2012), 210--224. Google ScholarDigital Library
- Ruochen Liu, Yangyang Chen, Wenping Ma, Caihong Mu, and Licheng Jiao. 2014. A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model. Soft Computing 18, 10 (2014), 1913--1929. Google ScholarDigital Library
- Ruochen Liu, Jing Fan, and Licheng Jiao. 2015. Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm. Applied Intelligence 43, 1 (2015), 192--207. Google ScholarDigital Library
- Arrchana Muruganantham, Kay Chen Tan, and Prahlad Vadakkepat. 2016. Evolutionary dynamic multiobjective optimization via Kalman filter prediction. IEEE transactions on cybernetics 46, 12 (2016), 2862--2873.Google Scholar
- Su Nguyen, Mengjie Zhang, Mark Johnston, and Kay Chen Tan. 2012. Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming. IEEE Transactions on Evolutionary Computation 18, 2 (2012), 193--208. Google ScholarDigital Library
- Mohammad Nabi Omidvar, Xiaodong Li, and Xin Yao. 2011. S-mart use of computational resources based on contribution for cooperative co-evolutionary algorithms. In Genetic and Evolutionary Computation Conference, GECCO 2011, Proceedings, Dublin, Ireland, July. 1115--1122. Google ScholarDigital Library
- Mitchell A Potter. 1997. The design and analysis of a computational model of cooperative coevolution. George Mason University.Google Scholar
- Christopher D Rosin and Richard K Belew. 1997. New method-s for competitive coevolution. Evolutionary Computation 5, 1 (1997), 1--29. Google ScholarDigital Library
- Provas Kumar Roy and Sudipta Bhui. 2015. A multi-objective hybrid evolutionary algorithm for dynamic economic emission load dispatch. International Transactions on Electrical Energy Systems 26, 1 (2015), 49--78.Google ScholarCross Ref
- Jiangjun Tang, Sameer Alam, Chris Lokan, and Hussein A Abbass. 2012. A multi-objective evolutionary method for dynamic airspace re-sectorization using sectors clipping and similarities. In Evolutionary Computation (CEC), 2012 IEEE Congress on. IEEE, 1--8.Google ScholarCross Ref
- Frans Van den Bergh and Andries Petrus Engelbrecht. 2004. A cooperative approach to particle swarm optimization. IEEE transactions on evolutionary computation 8, 3 (2004), 225--239. Google ScholarDigital Library
- Paul PY Wu, Duncan Campbell, and Torsten Merz. 2011. Multi-objective four-dimensional vehicle motion planning in large dynamic environments. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 41, 3 (2011), 621--634. Google ScholarDigital Library
- Biao. Xu, Yong Zhang, Dunwei Gong, Yinan Guo, and Miao Rong. 2017. Environment Sensitivity-based Cooperative Co-evolutionary Algorithms for Dynamic Multi-objective Optimization. IEEE/AGM Transactions on Computational Biology and Bioinformatics (2017).Google Scholar
- Qingfu Zhang, Aimin Zhou, Shizheng Zhao, Ponnuthurai Na-garatnam Suganthan, Wudong Liu, and Santosh Tiwari. 2008. Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition. University of Essex (2008).Google Scholar
- Zhuhong Zhang. 2008. Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Applied Soft Computing 8, 2 (2008), 959--971. Google ScholarDigital Library
- Aimin Zhou, Yaochu Jin, and Qingfu Zhang. 2014. A population prediction strategy for evolutionary dynamic multiobjective optimization. Cybernetics, IEEE Transactions on 44, 1 (2014), 40--53.Google ScholarCross Ref
- Aimin Zhou, Yaochu Jin, Qingfu Zhang, Bernhard Sendhoff, and Edward Tsang. 2007. Prediction-based population reinitialization for evolutionary dynamic multi-objective optimization. In Evolutionary Multi-Griterion Optimization. Springer, 832--846. Google ScholarDigital Library
- Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8, 2 (2000), 173. Google ScholarDigital Library
Index Terms
- A parallel multi-objective cooperative co-evolutionary algorithm with changing variables
Recommendations
A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy
In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve ...
A co-evolutionary multi-objective optimization algorithm based on direction vectors
Most real world multi-objective problems (MOPs) have a complicated solution space. Facing such problems, a direction vectors based co-evolutionary multi-objective optimization algorithm (DVCMOA) that introduces the decomposition idea from MOEA/D to co-...
Hyper multi-objective evolutionary algorithm for multi-objective optimization problems
Multi-objective optimization problems (MOPs) are very common in practice. To solve MOPs, many kinds of multi-objective evolutionary algorithms (MOEAs) are proposed. However, different MOEAs have different performances for different MOPs. Therefore, it ...
Comments