This paper presents an extensive study of a new metaheuristics algorithm called Smell Agent Optimization (SAO) on some CEC numerical optimization benchmark functions and Hybrid Renewable Energy System (HRES) engineering problems. The SAO implements the relationships between a smell agent and an object evaporating a smell molecule. These relationships are modelled into three separate modes called the sniffing, trailing and random modes. The sniffing mode simulates the smell perception capability of the agent as the smell molecules diffuse from a smell source towards the agent. The trailing mode simulates the capability of the agent to track the part of the smell molecules until its source is identified. Whereas, the random mode is a strategy employed by the agent to avoid getting stuck in local minima. Thirty-seven commonly used CEC benchmark functions, and HRES engineering problem are tested, and results are compared with six other metaheuristics methods. Experimental results revealed that the SAO can find the global optimum in 76% of the benchmark functions. Similarly, statistical results showed that the SAO also obtained the most cost effective HRES design compared to the benchmarked algorithms.

A Novel Smell Agent Optimization (SAO): An extensive CEC study and engineering application

The paper presents a modified BAT algorithm using cultural evolution process. We employ the normative knowledge inherent in cultural algorithm to modified the bat algorithm and the resulting algorithm is called CABAT_Ns. The CABAT_Ns is used to design a maximum power point tracking (MPPT) for a PV system. We carried out a number of simulations in MATLAB using two different shading conditions such as Shading Test Condition 1 and Shading Test Condition 2 (STC1 and STC2). Simulation was compared with standard bat algorithm and results showed that CABAT_Ns can obtain the global MPP of the PV system. effectively.

Cultured Bat Algorithm for Optimized MPPT Tracking Under Different Shading Conditions

There are increasing needs to develop control systems that can be used to automatically point Direct Current (DC) servomotor driven parabolic antennas to moving targets, notably in satellite tracking, to maintain the desired line of sight for quality communication. Although many researchers nowadays focus on artificial intelligence (AI) techniques, this work reckons that a well designed optimal linear controller can still give acceptable results at reduced system cost and complexity. In this paper the design and control of DC servomotor-based antenna positioning system has been addressed and simulated in MATLAB/SIMULINK software. The response of the system is analyzed and results are first obtained by using well tuned Proportional-Integral-Derivative (PID) controller. The PID controller is then replaced by Linear Quadratic Regulator (LQR) which apart from optimizing the system response increases the accuracy of the state variables by estimating the states. It has been shown that the LQR results are much better than the results obtained by PID controller in terms of reduced steady state error, settling time and overshoot. Moreover, since low speed rotations are required and assuming linear conditions, the results obtained with LQR method can challenge those obtained by AI based techniques.

DC Servomotor-based Antenna Positioning Control System Design using PID and LQR Controller

PID controller parameters enhanced founded on Artificial Fish Swarm Algorithm

Bonaventure Onyeka Ekengwu, Paulinus Chinaenye Eze, Christopher Nnaemeka Asiegbu, Samuel Chukwuemeka Olisa, Chimezie Felix Udechukwu Department of Electronic Engineering, University of Nigeria Nsukka, Nsukka, Nigeria Department of Electrical and Electronic Engineering, Federal University of Technology Owerri, Owerri, Nigeria Department of Electrical and Electronic Engineering, Covenant Polytechnic, Aba, Nigeria

International Journal of Informatics and Communication Technology (IJ-ICT)

This paper proposed a modification of the Artificial Fish Swarm Algorithm (AFSA) using adaptive behaviour base combination of normative and situational knowledge inherent in cultural algorithm. Four variations (wCAFSA_ Ns, wCAFSA_Sd, wCAFSA_NsSd and wCAFSA_NsNd) of the AFSA called weighted Cultural Artificial Fish Swarm Algorithm (wCAFSA) were then proposed with the hope of reducing the chance of falling into local minima by the standard AFSA. The performances of these were first evaluated using a collection of seven optimizations benchmark functions. Thereafter, all the variants were used to design an optimized PID controller for the dc motor of deep space antenna azimuth position control with the hope of minimizing the cost function and maximizing the steady state responses. Simulation results demonstrated the effectiveness of the algorithms in determining efficiently the optimized PID controller gains. Even though all the developed algorithms demonstrated their effectiveness, the wCAFSA_NsSd appears to be more efficient.

/pdf/optimal-design-of-pid-controller-for-deep-space-antenna-3wpoajer2d.pdf

Chung Jin Chan

Papers

Optimal Design of PID Controller for Deep Space Antenna Positioning Using Weighted Cultural Artificial Fish Swarm Algorithm