Research Article Joint Optimization of Interference and Cost in Cognitive Radio Heterogeneous Network Using Fuzzy Logic Powered Ants Shahzad Latif, 1,2 Suhail Akraam, 3 and Muhammad Aamer Saleem 4 1 School of Engineering & Applied Sciences, Isra University, Islamabad, Pakistan 2 Shaheed Zulfkar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan 3 International Islamic University, Islamabad, Pakistan 4 Hamdard Institute of Engineering & Technology, Hamdard University, Islamabad, Pakistan Correspondence should be addressed to Shahzad Latif; shahzadlatif97@gmail.com Received 24 April 2017; Revised 2 August 2017; Accepted 16 August 2017; Published 9 October 2017 Academic Editor: Xianfu Lei Copyright © 2017 Shahzad Latif et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Te advent of emerging wireless technologies has increased the demand of spectrum resources. On the other hand, present spectrum assignment is fxed and underutilized. Cognitive radio (CR) provides good solution to spectrum scarcity problem to accommodate new wireless applications. Te network selection is an important mechanism in cognitive radio heterogeneous network (CRHN) to provide optimal Quality of Service (QoS) to both Primary Users (PUs) and Secondary Users (SUs). Te aim of this work is to provide optimal QoS to SUs by appropriate network selection and channel assignment. Te proposed FLACSA selects the best network while maximizing the data rate and minimizing the interference and cost simultaneously. Simulation results show the attractive performance of our proposed algorithm. 1. Introduction Data transmission over wireless channel has been increased exponentially during the last decade. Due to this heavy load, wireless systems are facing problem of spectrum scarcity. To solve this problem, either expand wireless spectrum or use available spectrum efciently and intelligently. Fifh- generation (5G) networks are expected to provide high data rates with good QoS. Tus, in the coming years, demand for high data rates will increase manyfold. Tere are diferent views about 5G architecture: how to cope with high data rates such as cognitive radios, small cells, light communication, and MIMO communication systems. Te 5G networks are considered as a heterogeneous network that consists of dif- ferent types of primary networks. Te small cells deployment can meet the demand of high data rates in 5G networks. Moreover, efcient spectrum sharing temporally and spatially ensures the coverage of 5G networks everywhere and all the time. CRN opportunistically utilized the spectrum of licensed networks (primary network). CRNs are becoming the best choice to use wireless spectrum efciently [1]. CRNs use the spectrum holes available in existing wireless networks. Te unlicensed users of CRN are known as SUs and licensed users of primary networks are known as PUs [2]. Tis may face a problem in selecting which licensed net- work to join because CRHN incorporates multiple primary networks with diferent price and capacity requirements. In CRHN, selection of a suitable network [3], keeping in view the price, interference, and capacity, is thus very important [4, 5]. Authors in [6] considered a network selection problem as a noncooperative game in which SUs were considered as play- ers that achieved equilibrium without cooperation. Markov decision process is proposed in [7] to maximize SUs through- put in a cognitive radio heterogeneous network. Many tech- niques, such as game theory based spectrum allocation [8], pricing, and auction mechanism based spectrum assignment [9–11], have been developed in the literature on spectrum assignment in CRHN. Multiobjective optimization is NP- hard problem in cognitive radio networks [12]. Evolutionary computing algorithms are good to approx- imate NP-hard problems due to their low computational complexity [12]. Evolutionary algorithms are inspired from Hindawi Wireless Communications and Mobile Computing Volume 2017, Article ID 1075252, 11 pages https://doi.org/10.1155/2017/1075252