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The application of three different categories such as swarm-based, physics-based, and evolutionary-based techniques to various optimization problems related to different fields gained importance due to accuracy, speed and least chance to fall in local minima. Although there are number of physics based optimization techniques available to solve the optimization problems, however, they suffer from their complexity and may require multiple runs to achieve the optimum value. This paper focuses on a simple physics based problem which outperforms few of the existing algorithms. This work investigates the physics-based Lens formula integration using a population-based approach. The contender for a population-level solution to picture obstructions created by a convex, concave, or combination of the two lenses is the item with multiple dimensional locations. The object's location is updated using the image position. The fundamental goals of population-based optimization algorithms are highlighted by the concave, convex, or combination of both lenses, which enhances the speed of exploration, exploitation, and local optimum avoidance of the search space. The focal length equation, controls how the object's picture is produced, may be altered by specifying a number of random and adaptive factors to highlight the exploitation and exploration of the search space. The capacity of the proposed algorithm for exploitation, exploration, utilisation of the search space, avoidance of local minima, and attainment of global maxima is validated using unimodal, multimodal, and composite benchmark functions. In addition, a variety of real-world engineering issues taken into account to verify the performance of the proposed algorithm. The observations compared with few fundamental optimization techniques of the same category and found the proposed solution better performing in some context than the existing ones. [ABSTRACT FROM AUTHOR]

• A novel grey wolf optimization (GWO) algorithm is proposed to estimate three unknown parameters of the PV module. • The proposed method is compared with another popular method named as particle swarm optimization (PSO) algorithm. • Three different PV modules are successfully tested via single diode model. • Different statistical errors are calculated, and it is found the minimum for the proposed method. This paper proposes a novel parameter estimation technique for photovoltaic (PV) module considering grey wolf optimization (GWO). Manufacturer data sheet of PV module does not provide all essential parameters of PV module for modelling. Therefore for accurate modelling of PV module, an efficient parameter estimation technique is necessary to estimate the unknown parameters of the PV module. Since conventional estimation methods do not estimate PV parameters with high accuracy, therefore bio-inspired techniques have drawn considerable attention toward it. GWO is a new optimization technique which exhibits high efficiency for optimizing nonlinear equations. Therefore GWO technique is explored here for parameter estimation of PV module under changing weather conditions. The proposed GWO technique is employed for parameter estimation of different PV modules under different test conditions. GWO technique is compared with another popular optimization technique named as particle swarm optimization (PSO) and found having better fitness value compared to PSO. The proposed model is validated through experimental data under different weather conditions and found matching accurately with measured values. Considering different statistical errors, the discussed model has been compared with other well-known models found in the literature. The results show the superiority of the proposed model with a minimum error for both current-voltage and power-voltage characteristics. [ABSTRACT FROM AUTHOR]

The component value selection for active analogue filter design is an important issue to improve the performances and to make compatible with existing parameters value. The Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are efficient intelligent evolutionary algorithms for solving optimization problems. Both techniques are used to minimize the total design error of a 4th order Butterworth low pass active filter. This would be realized with component values which are compatible with E12 series. In addition, stability of filter is guaranteed by minimization of gain sensitivity product. By considering the minimization of Gain Sensitivity Product (GSP), the sixteen variables of objective function are reduced to eight variables which speed up the iteration process. The simulation results prove the efficiency of algorithms for the design of analogue active filter by optimizing the component values based on E12 compatible with minimization of GSP by minimising the design error. [ABSTRACT FROM AUTHOR]