Research And Application Of Two Heuristic Optimization Algorithms

Heuristic optimization algorithm is a new method of optimization algorithm based on co-evolution of a group of individuals in recent years. Compared with traditional optimization algorithm, Heuristic optimization algorithm has its advantages of intelligence, wide applicability, parallelism and global search ability, and has been widely used in modernization of national defense, control engineering, and economic dispatch, etc. Many branches are included by Heuristic algorithm, this paper focuses on the analysis, application and improvement of differential evolution algorithm and harmony search algorithm.Differential evolution algorithm belongs to a kind of heuristic algorithm branch, which includes:variation, crossover and selection options. Differential evolution algorithm is simple in structure. But once control parameter selection is not suitable, the algorithm often appears premature convergence, etc. A kind of extension variable dimensions of adaptive differential evolution algorithm is proposed in this paper (EVSDE), the parameter of DE algorithm is used as one-dimensional component of variation in the algorithm with optimum process of DE algorithm, which can achieve appropriate control parameter and improve the algorithm optimization ability. In addition, a new kind of mutation is proposed to keep balance between the global search and local search capability.Reliability problem is an issue to implement system reliability maximization by selecting the optimal combination of components and redundancy levels to meet resource constraints. EVSDE is applied in this paper for three typical systems of reliability problem, and is compared to two other differential algorithms in order to illustrate the stronger convergence and stability in solving the system reliability problem. On the basis of comparison, EVSDE algorithm has stronger convergence and stability with respect to the problem of solving the system reliability.Harmony search (HS) is a new meta-heuristic optimization method inspired by the music improvisation process. The HS lacks the capability of global search, which means it is easy to get trapped into the local optimum. This paper is inspired by the view of automatic control principle to put forth harmony search algorithm with a feedback adjusting (FAHS). FAHS algorithm is applied to0-1knapsack problem, and is compared to two other harmony search algorithms to show that FAHS algorithm is better than others in solving0-1knapsack problem.