GPU-based variation of parallel invasive weed optimization algorithm for 1000D functions

Abstract

Considering the problems of slow convergence and easily getting into local optimum of intelligent optimization algorithms in finding the optimal solution to complex highdimensional functions, we have proposed an improved invasive weed optimization (IIWO). Concrete adjustments include setting the newborn seeds per plant to a fixed number, changing the initial step and final step to adaptive one, and re-initializing the solution which exceeds the boundary value. Meanwhile, through applying the algorithm to the GPU platform, a parallel IIWO (PIIWO) based on GPU is obtained. The algorithm not only improves the convergence, but also strikes a balance between the global and local search capabilities. The simulation results of solving on the CEC' 2010 1000-dimensional (1000D) functions, have shown that, compared with other algorithms, our designed IIWO can yield better performance, faster convergence, higher accuracy and stronger robustness; whilst the PIIWO has significant speedup than the IIWO.

An Efficient and Effective Algorithm for Large Scale Global Optimization Problems

“…Theoretical research and practical application based on IWO are of great academic signi¯cance and practical value. Currently, IWO algorithm has been applied in manȳ elds, such as multiobjective optimization problem, 26 multi-criteria path optimization problem, 31 parameter estimation problem, 1 multimodal function optimization problem, 36 antenna array beamformer design problem, 53°o w shop scheduling problem, 4,55 antenna array optimization problem, 52 clustering problem, 32 DAG task scheduling problem, 19 LSGO problem, 28 traveling salesman problem, 56 and economic dispatch problem of power systems. 3 Particle swarm optimization (PSO) is an e±cient intelligence algorithm, 17 and it has been widely adopted for solving di®erent kinds of problems, such as multimodal optimization, 34 high-dimensional optimization, 15 optimal cycle program, 13 population classi¯cation, 54 network clustering, 14 parameter estimation, 30,29 multiobjective optimization.…”