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A Novel Evolutionary Algorithm for Constrained Optimization

Hu Yibo, Wang Yuping (School of Science, Xidian University, Xi′ an 710071, China)  
The penalty function is the most commonly used approach to constrained optimization, but the definition of the penalty factor is difficult and unavoidable. The new approach presented in this paper overcomes the difficulty. Its basic concept is to adopt two programs to evaluate the feasible and infeasible solutions respectively. It evaluates the feasible solutions according to the value of the objective function and the infeasible ones according to the degree of the violation of the constraints. The fitting function which can evaluate the points maps the feasible solutions to (-1,1) and the infeasible ones to (1,2). Thus the proposed algorithm can distinguish between the feasible solutions and the infeasible ones effectively. Numerical experiments are made and the results show that the proposed algorithm is feasible.
【Fund】: 国家自然科学基金资助(编号:60374063)
【CateGory Index】: TP18
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