Artificial Plant Optimization Algorithm for Constrained Optimization Problems

Abstract

Artificial plant optimization algorithm is proposed to solve constrained optimization problems in this paper. In APOA, a shrinkage coefficient is introduce to ensure that all dimensions of a branch are within lower and upper bounds, and a new function to determine whether the particle is within the feasible region. One dimensional search optimization methods are selected in algorithm to produce a new position which is guaranteed to be in the feasible region for the branch which escapes from the feasible region. The experimental results show that artificial plant optimization algorithm is effective and efficient for constrained optimization problems.