Pareto Artificial Life Algorithm for Multi-Objective Optimization

Pareto Artificial Life Algorithm for Multi-Objective Optimization

Jin-Dae Song (Hyosung Ebara Engineering Co., Korea) and Bo-Suk Yang (Pukyong National University, Korea)
Copyright: © 2013 |Pages: 16
DOI: 10.4018/978-1-4666-3625-5.ch008


Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions for the optimum design of the defined journal bearing. By comparing with the optimum solutions of a single objective function, it is confirmed that the single function optimization result is one of the specific cases of Pareto set of optimum solutions.
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Multi-Objective Optimization Problems

A MOP is defined as a problem which has two or more objective functions. A general MOP is defined asMinimize 978-1-4666-3625-5.ch008.m01(1) subject to 978-1-4666-3625-5.ch008.m02(2)978-1-4666-3625-5.ch008.m03, 978-1-4666-3625-5.ch008.m04(3) where fi(x) is the set of k objective functions, ci(x) is the set of m constraints, xj is the n optimization parameters, and S ∈ Rn is the solution or parameter space. Obtainable objective vectors {F(x)|x ∈ S} are denoted as Y, where Y ∈ Rk is usually referred to the attribute space.

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