Search

Estimation of Distribution Algorithm (EDA) is a class of evolutionary algorithms which construct the probabilistic model of the search space and generate new solutions according to the probabilistic model. Particle Swarm Optimization (PSO) is an algorithm that simulates the behavior of birds flocks and has good local search ability. This paper proposes a combination (EDAPSO) of EDA with PSO for the global optimization problems. The EDAPSO proposed in this paper combines the exploration of EDA with the exploitation of PSO. EDAPSO can perform a global search over the entire search space with faster convergence speed. EDAPSO has two main steps. First, the algorithm generates new solutions according to the probabilistic model. Then, EDAPSO updates the whole population according to improved velocity updating equation. EDAPSO has been evaluated on a series of benchmark functions. The results of experiments show that EDAPSO can produce a significant improvement in terms of convergence speed, solution accuracy and reliability.

Inspired by the search method of harmony search algorithms, we propose an other version of improved harmony search(IHS)algorithm to solve integer programming problems. This paper designs a novel improvising strategy which is beneficial to the convergence of algorithm in the early stage of iteration and increasing the diversity of population in the late stage of iteration. Experimental results show that the proposed algorithm can be an efficient alternative for solving integer programming problems.

The reactive power optimization problem has a significant influence on secure and economic operation of power systems. Reactive power optimization of power systems is a kind of mixed nonlinear integer programming problem and can reduce network loss by changing transformer taps, adjusting generator terminal voltage and connecting shunt capacitors. This paper proposes global differential evolution (GDE) algorithm which can improve the convergence speed and avoid local optimum. GDE is utilized to solve the reactive power optimization problems.Experimental results show that GDE algorithm is an effective algorithm to solve reactive power optimization problem and its optimization result is better than other algorithms.