A bi-objective based hybrid evolutionary-classical algorithm for handling equality constraints

Datta, Rituparna ; Deb, Kalyanmoy (2011) A bi-objective based hybrid evolutionary-classical algorithm for handling equality constraints Lecture Notes in Computer Science, 6576 . pp. 313-327. ISSN 0302-9743

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Official URL: http://www.springerlink.com/content/j8602149247u73...

Related URL: http://dx.doi.org/10.1007/978-3-642-19893-9_22


Equality constraints are difficult to handle by any optimization algorithm, including evolutionary methods. Much of the existing studies have concentrated on handling inequality constraints. Such methods may or may not work well in handling equality constraints. The presence of equality constraints in an optimization problem decreases the feasible region significantly. In this paper, we borrow our existing hybrid evolutionary-cum-classical approach developed for inequality constraints and modify it to be suitable for handling equality constraints. This modified hybrid approach uses an evolutionary multi-objective optimization (EMO) algorithm to find a trade-off frontier in terms of minimizing the objective function and the constraint violation. A suitable penalty parameter is obtained from the frontier and then used to form a penalized objective function. The procedure is repeated after a few generations for the hybrid procedure to adaptively find the constrained minimum. Unlike other equality constraint handling methods, our proposed procedure does not require the equality constraints to be transformed into an inequality constraint. We validate the efficiency of our method on six problems with only equality constraints and two problems with mixed equality and inequality constraints.

Item Type:Article
Source:Copyright of this article belongs to Springer.
Keywords:Evolutionary Multi-objective Optimization; Constraint Handling; Equality Constraint; Penalty Function; Bi-Objective Optimization; Hybrid Methodology
ID Code:81008
Deposited On:03 Feb 2012 11:55
Last Modified:03 Feb 2012 11:55

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