Abstract
The Constriction Coefficient-based Particle Swarm Optimization and Chaotic Gravitational Search Algorithm (CPSOCGSA) is a stochastic hybrid optimization technique. It is a combination of PSO which is inspired by the simulated behavior of bird flocking and GSA that is a physics-based heuristic technique inspired by Newton’s law of universal gravitation. It combines the exploitation and exploration capabilities of PSO and GSA, respectively, to obtain the optimal result. Further, it uses ten chaotic maps for the proper balance between exploration and exploitation processes. The advantages of CPSOCGSA have been incorporated in various mechanical and civil engineering design frameworks which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), and Three Bar Truss Design (TBTD). The CPSOCGSA has been compared with seven stochastic algorithms, particularly Standard Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CPSOCGSA was successful in minimizing the cost function and handling the constraints of the design frameworks efficiently as compared to other algorithms.
Published Version
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