Swarm Directions Embedded Differential Evolution for Faster Convergence of Global Optimization Problems

Abstract

In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algorithm. This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence. The embedding of swarm directions to the basic DE algorithm is done with the help of a "switchover constant " called α which keeps a record of the contraction of search space. The proposed HDE algorithm is tested on a set of 10 unconstrained benchmark problems and four constrained real life, mechanical design problems. Empirical studies show that the proposed scheme helps in improving the convergence rate of the basic DE algorithm without compromising with the quality of solution.

Experimental assessment of maximum power extraction from solar panel with different converter topologies

“…literature. 3,4 In Atlas and Sharaf, Kumari and Babu, JP and Pierre, and Ali et al, 2,[5][6][7] SIMULINK/MATLAB model has been developed based on the basic circuit equations of the PV solar cells including the effects of solar irradiation and temperature changes. In Gazoli et al, 8 comprehensive approach of modeling and simulation of PV arrays is presented that proposed a method to determine the parameters of a single-diode model from panel.…”