Research On Artificial Bee Colony Based Hybrid Optimization Algorithms And Applications

Artificial bee colony (ABC) algorithm is a kind of relatively new bionic intelligent algorithm. Due to its unique mechanisms, such as division of labor, information exchange, it has attracted widespread attention since it proposed. Artificial bee colony algorithm has simple structure and clear concept, furthermore, it is easy to implement and possess excellent global optimization performance. Therefore, it has broad application prospects. However, the classical ABC algorithm also has some drawbacks and deficiencies, such as slow convergence speed at later stage, weak local search capability, and it could not deal with discrete variables and problems with constraints. Inspired by the behavior of biological intelligence, the ABC algorithm is studied and improved in this dissertation, and then the proposed algorithms are applied to solve some complex engineering optimization problems.The main contributions of this dissatation are summarized as follows:(1) To solve complex unit-commitment scheduling problems with mixed variables and heavy constraints, the superiorities of Genetic Algorithm (GA) in discrete problems and ABC algorithm in continuous problems are combined and the ICGA-ABC algorithm based on repair operation is proposed. This algorithm adopted integer-coded (IC) chromosomes to represent the scheduling solutions intuitively, with shorter string lengths. The proposed repair operation improves the search capabilities of feasible solutions, and avoided the potential "combination explosion" The UC problem is then simplified to an Economic Load Distribution (ELD) problem and ABC is adopted to solve it efficiently. The ICGA-ABC is used to solve more UC problems with different numbers of generators, and the results show that it can obtain better schedulings than that of LR, BCGA and ICBF, which could result in the reduction of total power production cost.(2) The convergence speed of ABC algorithm will slow down much at later stage. Inspired by the foraging behavior of E.coli, chemotaxis effect is embedded into the ABC algorithm as local search strategy and the HABC algorithm is proposed. For selection operation, adaptive Boltzmann probability is adopted to adjust selective pressures, which could improve population diversity, avoid premature convergence, and accelerate the convergence velocity. The performances of proposed HABC are validated by8typical benchmark functions. The HABC is firstly applied to solve the parameter estimation problems of the PEM Fuel Cell model, and the results show that the optimized model by HABC can predict experimental data points more accurately, and can reflect the nonlinear property of the system better.(3) Inspired by RNA molecular, the three RNA operators are introduced into the ABC algorithm to improve population diversity and avoid trapping into local minima. The RNA-ABC algorithm is combined with Oracle penalty function technology to solve optimization problems with complex constraints. Discrete variables are changed to continuous ones and equality constraints are transformed to inequality constraints, which results in that Oracle penalty method can adaptively handle all kinds of constraints. The performance of the proposed algorithm has been validated by8typical benchmark functions with complex nonlinear constraints. The proposed algorithm is applied to solve short-term gasoline-blending scheduling problem. The experimental results show that the proposed approach can obtain better recipes than two other methods, and can reach a higher profit.(4) In order to improve the efficiency of artificial bee colony algorithm, especially for high-dimensional optimization problems, a variety of mutation and crossover operators from Differential Evolution (DE) algorithm are introduced into the employed bee phase of ABC, and the DABC algorithm based on the competition is proposed. Combined with the new vector generation policy and competitive mechanism, the leading role of employed bee phase has been enhanced, and the performance is improved. Numerical experiment on some typical benchmark functions demonstrate that the hybrid algorithm has speed up the search process of solving high-dimensional problems. Then, the proposed algorithm is used to train and optimize the RBF neural network model for modeling the overhead cranes system. The experimental results show that the DABC algorithm can obtain satisfactory RBF network model with excellent fitting accuracy and generalization ability.