New Dynamic Multi-Objective Constrained Optimization Evolutionary Algorithm

Chun-An Liu (), Yuping Wang () and Aihong Ren ()
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Chun-An Liu: Department of Mathematics, Baoji University of Arts and Sciences, Baoji, Shaanxi 721013, P. R. China
Yuping Wang: School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, P. R. China
Aihong Ren: Department of Mathematics, Baoji University of Arts and Sciences, Baoji, Shaanxi 721013, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2015, vol. 32, issue 05, 1-23

Abstract: For dynamic multi-objective constrained optimization problem (DMCOP), it is important to find a sufficient number of uniformly distributed and representative dynamic Pareto optimal solutions. In this paper, the time period of the DMCOP is first divided into several random subperiods. In each random subperiod, the DMCOP is approximately regarded as a static optimization problem by taking the time subperiod fixed. Then, in order to decrease the amount of computation and improve the effectiveness of the algorithm, the dynamic multi-objective constrained optimization problem is further transformed into a dynamic bi-objective constrained optimization problem based on the dynamic mean rank variance and dynamic mean density variance of the evolution population. The evolution operators and a self-check operator which can automatically checkout the change of time parameter are introduced to solve the optimization problem efficiently. And finally, a dynamic multi-objective constrained optimization evolutionary algorithm is proposed. Also, the convergence analysis for the proposed algorithm is given. The computer simulations are made on four dynamic multi-objective optimization test functions and the results demonstrate that the proposed algorithm can effectively track and find the varying Pareto optimal solutions or the varying Pareto fronts with the change of time.

Keywords: Dynamic optimization; multi-objective constrained optimization; Pareto optimal solution; Pareto front; time parameter (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0217595915500360

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