Solving Numerical Optimization Problems by Simulating Particle-Wave duality and Sozial Information Sharing

The demand for the solutions to different complex numerical optimization problems has long outstripped the ability of engineers to supply them. Since the numerical optimization is a static problem that is naturally similar to the movement of particulates with particle-wave duality in potential field, it can be simulated by the assistance of cooperative searching agents with social information sharing. A paradigm evolution algorithm PACA is realized with few parameters. The experiments by comparing PACA with genetic algorithms (GA) and particle swarm optimization (PSO) on some famous benchmark functions show that it can get high-quality solutions efficiently.