Optimization is an important issue in many areas of science and engineering.At present,there are many ways to solve the optimization problem,and Artificial Bee Colony(ABC)is one of them.This algorithm is a new kind of swarm intelligence optimization algorithm proposed by well-known scholar Karaboga in 2005,which is widely concerned because of the advantages of parallel exploitation and development,fewer control parameters,simple and easy to understand.Compared with other swarm intelligence optimization algorithms,such as Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO),artificial bee colony algorithm has been proved to be better performance and is a competitive optimization algorithm.However,the algorithm still has some shortcomings such as weak local search ability,slow convergence speed and poor solution.Based on the deep understanding of the theory of artificial bee colony algorithm and the research of the existing improved schemes,this paper studies the above deficiencies and improves the basic artificial bee colony algorithm to solve single-objective optimization problems and multi-objective optimization problems,and then is applied to Environment/Economic Dispatch Problem(EED).The main work of this thesis can be summarized as follows:1)A single-objective artificial bee colony optimization algorithm based on enhancing the local search ability is proposed.The algorithm first adopts the high-dimensional Lorentz chaotic system in the initial stage to obtain the initial population with good ergodicity and uniform distribution.Then,search strategy is improved inspired by the differential evolution algorithm,using the best individuals in the current population to guide the next generation to update so that the local search capabilities of the algorithm is enhanced.Meanwhile,the parameter is adjusted in order to improve the universality of the algorithm.2)A single-objective artificial bee colony optimization algorithm with balanced search ability is proposed.In order to obtain a higher quality initial population and reduce the number of optimization iterations,the GOBL(Generalized Opposition-Learning Strategy)is introduced.Inspired by the differential evolution algorithm,the search equation of the employed bees and the onlooker bees is adjusted to two new search equations that one of them is used to enhance the local search ability and the other is used to avoid premature convergence of the post-optimization process.Moreover,the algorithm adjusts the framework of the basic artificial bee colony algorithm to improve convergent performance.3)A multi-objective artificial bee colony optimization algorithm based on multi-search strategy is proposed.At present,most of the multi-objective artificial bee colony optimization algorithms only use a single search strategy to search.In this paper,a new multi-objective artificial bee colony optimization algorithm using multiple search strategies is proposed.This algorithm adopts decomposition method to convert MOP into a set of aggregation problems.Then all individuals are assigned accordingly to optimize each aggregation problem,and two search strategies are used to help speed convergence and maintain population diversity.After that,the non-dominated solutions are stored in external archives and further execute the evolutionary search strategy to exchange useful information between them.The effectiveness is proved through the test function,and the algorithm is used to solve the EED problem with excellent performance. |