www.astesj.com 995 Advanced Design of Current-mode Pass-band Filter using Ant Colony Optimization Technique Kritele Loubna 1,* , Benhala Bachir 2 , Zorkani Izeddine 1 1 Faculty of Sciences Dhar el Mahraz, University of Sidi Mohamed Ben Abdllah, Fez, 30050, Morroco 2 Faculty of Sciences, University of Moulay Ismail, Meknes, 50000, Morroco A R T I C L E I N F O A B S T R A C T Article history: Received: 02 September, 2020 Accepted: 02 November, 2020 Online: 08 December, 2020 Ant Colony Optimization (ACO) algorithm, a well-known robust technique to solve easily both a simple and multiple objective optimization problems. This article presents an application of the ACO in order to achieve the optimal sizing of analog circuit. The proposed technique is employed to optimizing the sizing of a positive second-generation current conveyor (CCII+). Results show better objective functions than previously achieved by other optimization procedure. PSPICE simulations are used to confirm the validity of the reached optimal results and the accuracy of the proposed procedure. Keywords: Ant colony optimisation Analog circuit design Positive second-generation current conveyor 1. Introduction Current Conveyor (CC) represents the first construction block developed for current signal processing [1]. Two years later, in 1970, another variant of a CC, namely, Second Generation Current Conveyor (CCII) was published [2]. Recently, a Current Conveyor is seen as a good alternative for the voltage-mode. CC can be widely employed in different applications. The CCII is the most popular configuration [3]. Despite the number of positive proprieties ensured by the CCS [4], it seems that designing integrated CC circuits with high performance is in effect still open particularly in CMOS technology [5]. Currently, an important interest has been devoted to achieve the best sizing of the analog component efficiently and with high accuracy. Some (meta) heuristics were used in the scientific publications such as Genetic Algorithms (GA) [6, 7], Ant Colony Optimization (ACO) [8, 9], Particle Swarm Optimization (PSO) [10], Differential Evolution algorithm (DE) [11], Simulated Annealing (SA) [12] and Artificial Bee Colony Algorithm (ABC) [13, 14]. ACO algorithm is proposed in this work, with view to get an optimal design of positive second-generation current conveyor (CCII+). To this end, we applied three of the most important variants of ACO, specifically, the Ant System (AS), the Min-Max Ant System (MMAS) and the Ant Colony System (ACS). In fact, the ACO has already efficiently been used to treat real optimization problems, particularly in the analog circuit area. A high performance CCII+ depends on the high-end cut-off frequency for the current waveform (Fci) and the input impedance at port X. Hence, two objectives functions are taken into consideration: achieve a low input resistance RX and a very high cut-off frequency Fci. However, the suggested technique is a mono- objective one, to this end; the optimization process is treated using two distinct manners; firstly, each objective function is solved separately. In the second way, a mono-objective problem is considered by using a weighting technique. This paper is organized in the following manner: The second section is about a detail description of the ACO technique. The third section proposes an example for the optimal sizing of CCII+. Then, a comparison with some similar works and simulation results are represented in the fourth section. Through the fifth section, the optimized CCII+ is exploited in order to form a current mode filter. Finally, the sixth section is the conclusion. 2. The ACO Technique Presentation Ant Colony Optimization (ACO) is a nature-inspired algorithm, used to resolve various hard problems. The use of ACO has proved a capacity to deal with complex optimization problems successfully, for instance we can cite the traveling salesman ASTESJ ISSN: 2415-6698 * Corresponding Author: Kritele Loubna, loubnakritele@gmail.com Advances in Science, Technology and Engineering Systems Journal Vol. 5, No. 6, 995-1000 (2020) www.astesj.com Special Issue on Innovative Research in Applied Science, Engineering and Technology https://dx.doi.org/10.25046/aj0506119