.png){kind=icon}
返回
A competitive mechanism integrated multi-objective whale optimization algorithm with differential evolution q
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2020.12.065.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, a competitive mechanism integrated whale optimization algorithm (CMWOA) is proposed to deal with multi-objective optimization problems. By introducing the novel competitive mechanism, a better leader can be generated for guiding the update of whale population, which benefits the convergence of the algorithm. It should also be highlighted that in the competitive mechanism, an improved calculation of crowding distance is adopted which substitutes traditional addition operation with multiplication operation, providing a more accurate depiction of population density. In addition, differential evolution (DE) is concatenated to diversify the population, and the key parameters of DE have been assigned different adjusting strategies to further enhance the overall performance. Proposed CMWOA is evaluated comprehensively on a series of benchmark functions with different shapes of true Pareto front. Results demonstrate that proposed CMWOA outperforms other three methods in most cases regarding several performance indicators. Particularly, influences of model parameters have also been discussed in detail. At last, proposed CMWOA is successfully applied to three real world problems, which further verifies the practicality of proposed algorithm. CO 2020 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective problems
Whale optimization algorithm (WOA)
Competitive mechanism
Differential evolution (DE)
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
.png){kind=icon}
OAAI分享收藏分享收藏A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer一种基于Sigmoid函数的自适应加权粒子群优化算法OAAI
OAAI分享收藏The Predictive Validity of the GRE Across Graduate Outcomes: A Meta-Analysis of Trends Over Time
OAAI
OAAI分享收藏分享收藏Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems多目标进化算法的多样性评估: 性能度量和基准问题OAAI
OAAI分享收藏
查看更多内容