May 19, 2022 · This paper proposes a multifactorial differential evolution algorithm with optima-based transformation (MFDE-OBT), which employs the optimal solution of each generation to design an improved assortative mating operation based on the DE/rand/2 mutation.

May 29, 2023 · The success-history based adaptive differential evolution algorithm (SHADE) is used as the search engine, which can demonstrate the generality of the MFO paradigm. Three multifactorial optimization benchmark sets are used to verify the …

In order to solve this issue, this article proposes multifactorial differential evolution algorithm with intermediate population (MFDE-IP). The intermediate population is obtained by linear weighting of optimal individuals for different tasks. In addition, a new mutation strategy is designed to guide the transfer of the population.

Apr 1, 2021 · By considering several spanning tree optimization problems as a MFO problem, we introduce Multi-tasking Local Search Evolutionary Algorithm (M-LSEA), which is an advanced version of MFEA applying local refinement methods. …

Apr 1, 2023 · Then, the helper objective-assisted multifactorial differential evolutionary algorithm is proposed, termed h-MFDE. Experimental results show that h-MFDE is competitive in finding the global optimal on complex optimization problems comparing with other state-of-the-art EAs.

Jul 6, 2018 · This paper presents 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.

Jul 13, 2018 · Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based evolutionary algorithm and provides an efficient way to evolve individuals for solving multiple tasks concurrently. Its efficiency is derived by implicitly transferring the genetic information among tasks.

However, most of MFEAs tend to suffer from premature convergence. To deal with this issue, this article designs a novel MFEA by integrating differential evolution, and a hybrid of global and local search strategies, named MFDE-GLS for short.

To fill this gap, we propose a new MFEA based on diffusion gradient descent (DGD), namely, MFEA-DGD in this article. We prove the convergence of DGD for multiple similar tasks and demonstrate that the local convexity of some tasks can help other tasks escape from local optima via knowledge transfer.

This paper proposes a multifactorial differential evolution algorithm with optima-based transformation (MFDE-OBT), which employs the optimal solution of each generation to design an improved assortative mating operation based on the DE/rand/2 mutation.