Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
The DynCOAA algorithm for dynamic constraint optimization problems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Solving dynamic constrained optimization problems with asynchronous change pattern
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Infeasibility driven evolutionary algorithm with ARIMA-based prediction mechanism
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Memory design for constrained dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Solving dynamic constraint optimization problems using ICHEA
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Many real-world dynamic optimisation problems have constraints, and in certain cases not only the objective function changes over time, but the constraints also change as well. However, in academic research there is not many research on continuous dynamic constrained optimization, and particularly there is little research on whether current numerical dynamic optimization algorithms would work well in dynamic constrained environments nor there is any numerical dynamic constrained benchmark problems. In this paper, we firstly investigate the characteristics that might make a dynamic constrained problems difficult to solve by existing dynamic optimization algorithms. We then introduce a set of numerical dynamic benchmark problems with these characteristics. To verify our hypothesis about the difficulty of these problems, we tested several canonical dynamic optimization algorithms on the proposed benchmarks. The test results confirm that dynamic constrained problems do have special characteristics that might not be solved effectively by some of the current dynamic optimization algorithms. Based on the analyses of the results, we propose a new algorithm to improve the performance of current dynamic optimization methods in solving numerical dynamic constrained problems. The test results show that the proposed algorithm achieves superior results compared to the tested existing dynamic optimization algorithms.