Abstract
Network design is one of the most important and most frequently encountered classes of optimization problems. It is a combinatory field in combinatorial optimization and graph theory. When considering a bicriteria network design (bND) problem with the two conflicting objectives of minimizing cost and maximizing flow, network design problems where even one flow measure be maximized, are often NP-hard problems. But, in real-life applications, it is often the case that the network to be built is required to optimize multi-criteria simultaneously. Thus the calculation of the multi-criteria network design problems is a difficult task. In this paper, we propose a new multiobjective hybrid genetic algorithm (mo-hGA) hybridized with Fuzzy Logic Control (FLC) and Local Search (LS). Numerical experiments show the effectiveness and the efficiency of our approach by comparing with the recent researches.
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Gen, M., Lin, L., Jo, JB. (2009). Evolutionary Network Design by Multiobjective Hybrid Genetic Algorithm. In: Gen, M., et al. Intelligent and Evolutionary Systems. Studies in Computational Intelligence, vol 187. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95978-6_8
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DOI: https://doi.org/10.1007/978-3-540-95978-6_8
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