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This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuous-time linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output and the actual output is reduced. The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon’s rank test. The experimental findings show that the hMVOSCA can produce a Hammerstein system that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA outperforms other popular metaheuristic algorithms.

Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. In literature, ACS has been employed to solve DNA sequence design problem. The DNA sequence design problem was modelled based on a finite state machine in which the nodes represent the DNA bases {A, C, T, G}. Later in 2011, an extended computational model of finite state machine has been employed for DNA sequence design using ACS. The performance evaluation, however, was limited. In this study, the extended computational model of finite state machine is revisited and an extensive performance evaluation is conducted using 5, 7, 10, 15, 20, 25, 30, 35, and 40 agents/ants, each with 100 independent runs. The performance of the extended computational model is also benchmarked with the existing algorithm such as a Genetic Algorithm (GA), Multi-Objective Evolutionary Algorithm (MOEA), and Particle Swarm Optimisation (PSO). [ABSTRACT FROM AUTHOR]

Multi Objective Optimization (MOO) problem involves simultaneous minimization or maximization of many objective functions. Various MOO algorithms have been introduced to solve the MOO problem. Traditional gradient-based techniques are one of the methods used to solve MOO problems. However, in the traditional gradient-based technique only one solution is generated. Thus, an alternative approach such as Particle Swarm Optimization (PSO), which able to produce a number of possible solutions are highly desirable. In PSO, particles search the optimum solution under the influence of a better solution known as leader. This leader facilitates cooperation between all particles. However, this strategy to select the leader has to be changed when it is used for MOO problems. This paper presents an overview of the multi-objective PSO algorithms, which emphasize on the leader selection. In addition, the description of PSO and multi-objective optimization problems are also provided. [ABSTRACT FROM AUTHOR]