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A Smart Structural Algorithm (SSA) Based on Infeasible Region to Solve Mixed Integer Problems

A Smart Structural Algorithm (SSA) Based on Infeasible Region to Solve Mixed Integer Problems

Mohammad Hassan Salmani, Kourosh Eshghi
Copyright: © 2017 |Volume: 8 |Issue: 1 |Pages: 21
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781522513216|DOI: 10.4018/IJAMC.2017010102
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MLA

Salmani, Mohammad Hassan, and Kourosh Eshghi. "A Smart Structural Algorithm (SSA) Based on Infeasible Region to Solve Mixed Integer Problems." IJAMC vol.8, no.1 2017: pp.24-44. http://doi.org/10.4018/IJAMC.2017010102

APA

Salmani, M. H. & Eshghi, K. (2017). A Smart Structural Algorithm (SSA) Based on Infeasible Region to Solve Mixed Integer Problems. International Journal of Applied Metaheuristic Computing (IJAMC), 8(1), 24-44. http://doi.org/10.4018/IJAMC.2017010102

Chicago

Salmani, Mohammad Hassan, and Kourosh Eshghi. "A Smart Structural Algorithm (SSA) Based on Infeasible Region to Solve Mixed Integer Problems," International Journal of Applied Metaheuristic Computing (IJAMC) 8, no.1: 24-44. http://doi.org/10.4018/IJAMC.2017010102

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Abstract

Optimization is an important fields of study in science where researchers seek to make the best and most practical decisions. Solving real optimization problems is an intractable issue which calls for generating an approximate using meta-heuristic algorithms. This study proposes a meta-heuristic algorithm which mainly searches the infeasible region. In this approach, the authors start from an infeasible solution, and while they try to get near to the feasible region, they ensure that the best value is kept for the objective function. The algorithm examines the space in such terms as Infeasibility and Objective Functions, Neighborhood Limited Area, Random Smart Points, and the calculation of new solutions. The algorithm can convert an infeasible solution to an appropriate corresponding feasible solution by applying a simple mathematical methodology. Finally, to test the efficiency of our algorithm, a sample random MIP problem and a hard benchmark TSP instance are solved and discussed in detail.

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