Hybrid evolutionary algorithm

Before a new stage decomposition based hybrid evolutionary algorithm is proposed in Section 9.4, we briefly review the algorithm for general 2S-MILPs of Caroe and Schultz [11] which is regarded as the state-of-the-art exact algorithm for 2S-MILPs... 

The hybrid evolutionary algorithm for 2S-MILPs is realized by using an evolution strategy (ES) to solve the master problem of the intensive 2S-MILP. Each individual of the ES represents a first-stage candidate solution x. The object parameters are encoded by a mixed-integer vector. The fitness of an individual is evaluated by the objective function of the master problem (MASTER),/ (x). 

In this section, the hybrid evolutionary algorithm described above is applied to a real-world scheduling problem under uncertainty. The performance of this algorithm is compared to that of the state-of-the-art MILP solver CPLEX and to that of... 

The mathematical model of two-stage stochastic mixed-integer linear optimization problems was discussed as well as state-of-the-art solution algorithms. A new hybrid evolutionary algorithm for solving this class of optimization problems was presented. The new algorithm exploits the specific problem structure by stage decomposition. 

Jalem, R., Mochiduki, Y., Nobuhara, K., Nakayama, M., and Nogami, M. (2012) Global minimum structure search in Li, Co02 composition using a hybrid evolutionary algorithm. Phys. Chem. Chem. Phys., 14 (37), 13095-13100. 

Some researchers have combined various optimization algorithms to improve the search efficiency and computational effort, including evolutionary algorithms (EA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (AGO), hybrid PSO-SQP, hybrid GA-ACO. Nevertheless, the combination of the GA and SQP algorithms is reported only in a few works [1,2]. 

Rodriguez, F.J., Garcia-Martinez, C., Lozano, M. Hybrid metaheuristics based on evolutionary algorithms and simulated anneaUng taxonomy, comparison, and synergy test. IEEE Trans. Evol. Comput. 16(6), 787-800 (2012)... 

Memetic algorithms, sometimes refered to as hybrid genetic algorithms, attempt to capture this behavior. At a simple level, they are similar to evolutionary algorithms. 

Rasmussen T.K. Krink, T. (2003). Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid. Bio Systems, Vol. 72, No. 1-2, pp. 5-17. 

Chiou, J.P. and Wang, F.S., 1999, Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process. Computers and Chemical Engineering, 23,1277. 

Knowles, J. (2006). ParEGO a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems, IEEE Transactions on Evolutionary Computation 10, 1, pp. 50-66. 

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