The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic optimization problems
Advances in evolutionary computing
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Evolving Dynamic Multi-Objective Optimization Problems with Objective Replacement
Artificial Intelligence Review
International Journal of Intelligent Systems
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Performance Measures for Dynamic Multi-Objective Optimization
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Journal of Artificial Intelligence Research
A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
The construction of dynamic multi-objective optimization test functions
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
A distributed Cooperative coevolutionary algorithm for multiobjective optimization
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
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Many real world problems are dynamic and multi-objective, which requires an optimisation algorithm to be able to continuously track the changing Pareto optimal set (POS) and Pareto optimal front (POF) over time. In this paper, a new variant of particle swarm optimisation (PSO) has been specifically designed by adaptively switching from competitive model to cooperative model to track for both POS and POF. In the proposed method, the competition is used to explore the search space, while the cooperation is applied to exploit the search space. The dynamic multi-objective functions are constructed to test the performance of the proposed algorithm. Both theoretical analysis and the numerical experiment have shown that the proposed algorithm is an excellent alternative for solving the dynamic multi-objective optimisation problems. Finally, the proposed method has been applied to the tuning of the parameters of PID controller for dynamic system in which a satisfactory control is obtained.