Zhijian Wu

TL;DR: An enhanced PSO algorithm called GOPSO is presented, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome the problem of premature convergence when solving complex problems.

TL;DR: A novel multi-strategy ensemble ABC (MEABC) algorithm, where a pool of distinct solution search strategies coexists throughout the search process and competes to produce offspring.

TL;DR: A novel algorithm based on generalized opposition-based learning (GOBL) to improve the performance of differential evolution (DE) to solve high-dimensional optimization problems efficiently and confirms that GODE outperforms classical DE, real-coded CHC (crossgenerational elitist selection, heterogeneous recombination, and cataclysmic mutation) and G-CMA-ES (restart covariant matrix evolutionary strategy) on the majority of test problems.

TL;DR: Simulation results demonstrate that GjODE is better than, or at least comparable to, six other algorithms, and employing GPU can effectively reduce computational time.

TL;DR: Experimental studies on 20 benchmark functions show that the PSO-STS and its variations can not only achieve better results, but also obtain faster convergence speed than the standard PSO.