.png){kind=icon}
返回
A scalable parallel cooperative coevolutionary PSO algorithm for multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2017.05.018.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first multi-objective cooperative coevolutionary algorithm based on PSO in the literature. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. In order to demonstrate the effectiveness of CCSMPSO, we compare our work with the original SMPSO and three different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II, CCSPEA2 and CCMOCell, along with their original sequential counterparts. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better or comparable results in terms of solution quality, when evaluated against three other CC algorithms and four state-of-the-art optimizers (namely SMPSO, NSGA-II, SPEA2, and MOCell), respectively. We then provide a scalability analysis, which consists of two studies. First, we analyze how the algorithms scale when varying the problem size, i.e., the number of variables. Second, we analyze their scalability in terms of parallelization, i.e., the impact of using more computational cores on the quality of solutions and on the execution time of the algorithms. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front. (C) 2017 Elsevier Inc. All rights reserved.
Keyword:
Metaheuristics
Particle swarm optimization
Cooperative
Coevolutionary
Parallelism
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.8K
被引数:
4.8K
机构
引用论文
.png){kind=icon}
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏分享收藏
The particle swarm - Explosion, stability, and convergence in a multidimensional complex space多维复杂空间中的粒子群爆炸，稳定性和收敛性
PREAI
分享收藏Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution通过合作协同进化在并行多目标进化算法中实现超线性性能
PREAI
分享收藏
查看更多内容