Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

Abstract Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques
that have been extensively used to solve difficult problems in a wide variety of disciplines …

Due to the increasing complexity of optimization problems, distributed differential evolution
(DDE) has become a promising approach for global optimization. However, similar to the …

Evolutionary multiobjective optimization has been a research area since the mid-1980s, and
has experienced a very significant activity in the last 20 years. However, and in spite of the …

差分进化算法由于算法结构简单易于执行, 并且具有优化效率高, 参数设置简单,
鲁棒性好等优点, 因此差分进化算法吸引了越来越多研究者的关注. 本文概述了差分进化算法的 …

Multiobjective particle swarm optimization (MOPSO) algorithms have attracted much
attention for their promising performance in solving multiobjective optimization problems …

An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm,
based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight …

This article presents a new noniterative inverse modeling technique based on machine
learning regression and its applications to microwave design optimization. The proposed …

This paper describes a unified view of parallel evolutionary algorithms for multi-objective
optimization problems. The parallel optimization algorithms are detailed from both design …

In many applications of evolutionary algorithms, the time required to evaluate the fitness of
individuals is long and variable. When the variance in individual evaluation times is non …