Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
OEA_SAT: an organizational evolutionary algorithm for solving satisfiability problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A multiagent evolutionary algorithm for combinatorial optimization problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constrained layout optimization in satellite cabin using a multiagent genetic algorithm
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A multi-agent genetic algorithm for resource constrained project scheduling problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Taking inspiration from the interacting process among organizations in human societies, this correspondence designs a kind of structured population and corresponding evolutionary operators to form a novel algorithm, organizational evolutionary algorithm (OEA), for solving both unconstrained and constrained optimization problems. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate the interaction among organizations. In experiments, 15 unconstrained functions, 13 constrained functions, and 4 engineering design problems are used to validate the performance of OEA, and thorough comparisons are made between the OEA and the existing approaches. The results show that the OEA obtains good performances in both the solution quality and the computational cost. Moreover, for the constrained problems, the good performances are obtained by only incorporating two simple constraints handling techniques into the OEA. Furthermore, systematic analyses have been made on all parameters of the OEA. The results show that the OEA is quite robust and easy to use.