Evolutionary Algorithm for Zero-One Constrained Optimization Problems Based on Objective Penalty Function

Abstract

In many evolutionary algorithms, it is very important way to use penalty function as a fitness function in order to solve many integer optimization problems. In this paper, we first define a new objective penalty function and give its some properties for integer constrained optimization problems. Then, we present an algorithm with global convergence for integer constrained optimization problems in theory. Moreover, based on the objective penalty function, a simple novel evolutionary algorithm to solve the zero-one constrained optimization problems is developed. Finally, numerical results of several examples show that the proposed evolutionary algorithm has a good performance for some zero-one optimization problems.