Central force optimization algorithm for constrained optimization problems and its engineering applications.

In order to balance the abilities of global detective and local search, this paper proposed a modified central force optimization (MCFO) algorithm based on crossover and mutation for solving constrained optimization problems. The proposed algorithm constructed the initial population by using the principles of the good point set method was to strengthen the diversity of particles. The optimal particle and the randomly selected particle are carried out arithmetic crossover operator which led gradually the population to the global optimum. It utilized diversity mutation operator to avoid the premature convergence, and tested two well-known benchmark functions and two engineering optimization application problems. The results demonstrate that the MCFO algorithm is an effective method for differential constrained optimization problems. [ABSTRACT FROM AUTHOR]