.png){kind=icon}
返回
Multi agent collaborative search algorithm with adaptive weights
.png){kind=icon}
OA
AI
DOI：10.1111/exsy.13709.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS-AW). MACS is a multi-agent memetic scheme for multi-objective optimization originally developed to mix local and population-based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub-problem the agent had to solve; (ii) the population-based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS-AW is compared against some state-of-art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS-AW is applied to the solution of two real-life optimization problems and compared against MACS2.1. It will be shown that MACS-AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS-AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS-AW and its predecessor obtain same results.
Keyword:
adaptive parameter
evolutionary algorithm
multi agent collaborative search
multi-objective optimization
multiple operators
utility function
weight vector adjusting
期刊
IF:
2.3
论文数:
2.6K
被引数:
3.8K
机构
引用论文
.png){kind=icon}
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏分享收藏Optimization of dry compressive strength of groundnut shell ash particles (GSAp) and ant hill bonded foundry sand using ann and genetic algorithm基于人工神经网络和遗传算法对花生壳灰颗粒(GSAp)和蚁丘粘结铸造砂的干压强度优化OAAI
OAAI分享收藏
查看更多内容