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MOBES: A Multiobjective Evolution Strategy for Constrained Optimization Problems

  • March 1999
Authors:
Thanh Binh To at Environmental sensing and Driver Monitoring
Thanh Binh To
  • Environmental sensing and Driver Monitoring
Binh Ulrich Korn
Binh Ulrich Korn
Binh Ulrich Korn
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Abstract

. In this paper a new MultiOBjective Evolution Strategy (MOBES) for solving multiobjective optimization problems subject to linear and nonlinear constraints is presented. MOBES is based on the new concept of C-, F - and N -fitness, which allows systematically to handle constraints and (in)feasible individuals. The existence of niche infeasible individuals in every population enables to explore new areas of the feasible region and new feasible pareto-optimal solutions. Moreover, MOBES proposed a new selection algorithm for searching, maintaining a set of feasible pareto-optimal solutions in every generation. The performance of the MOBES can be successfully evaluated on two selected test problems. 1 Introduction 1.1 Multiobjective optimization problem The general multiobjective optimization problem with linear and nonlinear constraints can be formally stated as below: min x f(x) = min x (f 1 (x); f 2 (x); Delta Delta Delta ; fN (x)) where x = (x 1 ; x 2 ; Delta Delta...
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