A real coded genetic algorithm for solving integer and mixed integer optimization problems

In this paper, a real coded genetic algorithm named MI-LXPM is proposed for solving integer and mixed integer constrained optimization problems. The proposed algorithm is a suitably modified and extended version of the real coded genetic algorithm, LXPM, of Deep and Thakur [K. Deep, M. Thakur, A new...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 212; no. 2; pp. 505 - 518
Main Authors: Deep, Kusum, Singh, Krishna Pratap, Kansal, M.L., Mohan, C.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 15.06.2009
Elsevier
Subjects:
Calculus of variations and optimal control
Constrained optimization
Exact sciences and technology
Integer and mixed integer optimization problems
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Random search based techniques
Real coded genetic algorithms
Sciences and techniques of general use
Integer and mixed integer optimization problems
Real coded genetic algorithms
Random search based techniques
Constrained optimization
Optimization method
Penalty method
Search algorithm
Truncation
Integer
Integer and mixed integer optimization
Numerical analysis
Algorithm performance
Genetic algorithm
Applied mathematics
Mixed problem
Mathematical programming
problems
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:In this paper, a real coded genetic algorithm named MI-LXPM is proposed for solving integer and mixed integer constrained optimization problems. The proposed algorithm is a suitably modified and extended version of the real coded genetic algorithm, LXPM, of Deep and Thakur [K. Deep, M. Thakur, A new crossover operator for real coded genetic algorithms, Applied Mathematics and Computation 188 (2007) 895–912; K. Deep, M. Thakur, A new mutation operator for real coded genetic algorithms, Applied Mathematics and Computation 193 (2007) 211–230]. The algorithm incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints. Performance of the algorithm is tested on a set of twenty test problems selected from different sources in literature, and compared with the performance of an earlier application of genetic algorithm and also with random search based algorithm, RST2ANU, incorporating annealing concept. The proposed MI-LXPM outperforms both the algorithms in most of the cases which are considered.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2009.02.044