Directed searching optimization algorithm for constrained optimization problems

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.

Research highlights► We propose a directed searching optimization algorithm (DSO). ► The DSO adopts two important operations - position updating and genetic mutation. ► Position updating is beneficial to the convergence of the DSO. ► Genetic mutation is beneficial to preventing the premature convergence of the DSO. ► We adopt a common penalty function method to handle constraint violations.